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Complete API Reference

Auto-generated documentation for all napari MCP server functions.

Documentation Structure

This page shows both the MCP tool interface (what you call from AI assistants) and the implementation (NapariMCPTools class methods). All functions use NumPy-style docstrings with detailed parameter and return information.

MCP Tool Interface (server.py wrappers)

These are the functions exposed as MCP tools:

Napari MCP Server.

Exposes a set of MCP tools to control a running napari Viewer: layer control, viewer control (zoom, camera, dims), and a screenshot tool returning a PNG image as base64.

Functions

set_gui_executor 

set_gui_executor(executor: Any | None) -> None

Configure an executor that runs a callable on the GUI/main thread.

If None, operations execute directly in the current thread.

Source code in src/napari_mcp/server.py 
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def set_gui_executor(executor: Any | None) -> None:
    """Configure an executor that runs a callable on the GUI/main thread.

    If None, operations execute directly in the current thread.
    """
    global _GUI_EXECUTOR
    _GUI_EXECUTOR = executor

detect_viewers async 

detect_viewers() -> dict[str, Any]

Detect available viewers (local and external).

Returns:

Type Description
dict

Dictionary with information about available viewers
Source code in src/napari_mcp/server.py 
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async def detect_viewers() -> dict[str, Any]:
    """
    Detect available viewers (local and external).

    Returns
    -------
    dict
        Dictionary with information about available viewers
    """
    return await NapariMCPTools.detect_viewers()

init_viewer async 

init_viewer(title: str | None = None, width: int | str | None = None, height: int | str | None = None, port: int | str | None = None) -> dict[str, Any]

Create or return the napari viewer (local or external).

Parameters:

Name Type Description Default
title str

Optional window title (only for local viewer).

 None
width int

Optional initial canvas width (only for local viewer).

 None
height int

Optional initial canvas height (only for local viewer).

 None
port int

If provided, attempt to connect to an external napari-mcp bridge on this port (default is taken from NAPARI_MCP_BRIDGE_PORT or 9999).

 None

Returns:

Type Description
dict

Dictionary containing status, viewer type, and layer info.
Source code in src/napari_mcp/server.py 
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async def init_viewer(
    title: str | None = None,
    width: int | str | None = None,
    height: int | str | None = None,
    port: int | str | None = None,
) -> dict[str, Any]:
    """
    Create or return the napari viewer (local or external).

    Parameters
    ----------
    title : str, optional
        Optional window title (only for local viewer).
    width : int, optional
        Optional initial canvas width (only for local viewer).
    height : int, optional
        Optional initial canvas height (only for local viewer).
    port : int, optional
        If provided, attempt to connect to an external napari-mcp bridge on
        this port (default is taken from NAPARI_MCP_BRIDGE_PORT or 9999).

    Returns
    -------
    dict
        Dictionary containing status, viewer type, and layer info.
    """
    return await NapariMCPTools.init_viewer(
        title=title, width=width, height=height, port=port
    )

close_viewer async 

close_viewer() -> dict[str, Any]

Close the viewer window and clear all layers.

Returns:

Type Description
dict

Dictionary with status: 'closed' if viewer existed, 'no_viewer' if none.
Source code in src/napari_mcp/server.py 
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async def close_viewer() -> dict[str, Any]:
    """
    Close the viewer window and clear all layers.

    Returns
    -------
    dict
        Dictionary with status: 'closed' if viewer existed, 'no_viewer' if none.
    """
    return await NapariMCPTools.close_viewer()

session_information async 

session_information() -> dict[str, Any]

Get comprehensive information about the current napari session.

Returns:

Type Description
dict

Comprehensive session information including viewer state, system info, and environment details.
Source code in src/napari_mcp/server.py 
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async def session_information() -> dict[str, Any]:
    """
    Get comprehensive information about the current napari session.

    Returns
    -------
    dict
        Comprehensive session information including viewer state, system info,
        and environment details.
    """
    return await NapariMCPTools.session_information()

list_layers async 

list_layers() -> list[dict[str, Any]]

Return a list of layers with key properties.

Source code in src/napari_mcp/server.py 
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async def list_layers() -> list[dict[str, Any]]:
    """Return a list of layers with key properties."""
    return await NapariMCPTools.list_layers()

add_image async 

add_image(path: str, name: str | None = None, colormap: str | None = None, blending: str | None = None, channel_axis: int | str | None = None) -> dict[str, Any]

Add an image layer from a file path.

Parameters:

Name Type Description Default
path str

Path to an image readable by imageio (e.g., PNG, TIFF, OME-TIFF).

 required
name str

Layer name. If None, uses filename.

 None
colormap str

Napari colormap name (e.g., 'gray', 'magma').

 None
blending str

Blending mode (e.g., 'translucent').

 None
channel_axis int

If provided, interpret that axis as channels.

 None

Returns:

Type Description
dict

Dictionary containing status, layer name, and image shape.
Source code in src/napari_mcp/server.py 
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async def add_image(
    path: str,
    name: str | None = None,
    colormap: str | None = None,
    blending: str | None = None,
    channel_axis: int | str | None = None,
) -> dict[str, Any]:
    """
    Add an image layer from a file path.

    Parameters
    ----------
    path : str
        Path to an image readable by imageio (e.g., PNG, TIFF, OME-TIFF).
    name : str, optional
        Layer name. If None, uses filename.
    colormap : str, optional
        Napari colormap name (e.g., 'gray', 'magma').
    blending : str, optional
        Blending mode (e.g., 'translucent').
    channel_axis : int, optional
        If provided, interpret that axis as channels.

    Returns
    -------
    dict
        Dictionary containing status, layer name, and image shape.
    """
    return await NapariMCPTools.add_image(
        path=path,
        name=name,
        colormap=colormap,
        blending=blending,
        channel_axis=channel_axis,
    )

add_labels async 

add_labels(path: str, name: str | None = None) -> dict[str, Any]

Add a labels layer from a file path (e.g., PNG/TIFF with integer labels).

Source code in src/napari_mcp/server.py 
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async def add_labels(path: str, name: str | None = None) -> dict[str, Any]:
    """Add a labels layer from a file path (e.g., PNG/TIFF with integer labels)."""
    return await NapariMCPTools.add_labels(path=path, name=name)

add_points async 

add_points(points: list[list[float]], name: str | None = None, size: float | str = 10.0) -> dict[str, Any]

Add a points layer.

  • points: List of [y, x] or [z, y, x] coordinates
  • name: Optional layer name
  • size: Point size in pixels
Source code in src/napari_mcp/server.py 
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async def add_points(
    points: list[list[float]], name: str | None = None, size: float | str = 10.0
) -> dict[str, Any]:
    """
    Add a points layer.

    - points: List of [y, x] or [z, y, x] coordinates
    - name: Optional layer name
    - size: Point size in pixels
    """
    return await NapariMCPTools.add_points(points=points, name=name, size=size)

remove_layer async 

remove_layer(name: str) -> dict[str, Any]

Remove a layer by name.

Source code in src/napari_mcp/server.py 
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async def remove_layer(name: str) -> dict[str, Any]:
    """Remove a layer by name."""
    return await NapariMCPTools.remove_layer(name)

set_layer_properties async 

set_layer_properties(name: str, visible: bool | None = None, opacity: float | None = None, colormap: str | None = None, blending: str | None = None, contrast_limits: list[float] | None = None, gamma: float | str | None = None, new_name: str | None = None) -> dict[str, Any]

Set common properties on a layer by name.

Source code in src/napari_mcp/server.py 
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async def set_layer_properties(
    name: str,
    visible: bool | None = None,
    opacity: float | None = None,
    colormap: str | None = None,
    blending: str | None = None,
    contrast_limits: list[float] | None = None,
    gamma: float | str | None = None,
    new_name: str | None = None,
) -> dict[str, Any]:
    """Set common properties on a layer by name."""
    return await NapariMCPTools.set_layer_properties(
        name=name,
        visible=visible,
        opacity=opacity,
        colormap=colormap,
        blending=blending,
        contrast_limits=contrast_limits,
        gamma=gamma,
        new_name=new_name,
    )

reorder_layer async 

reorder_layer(name: str, index: int | str | None = None, before: str | None = None, after: str | None = None) -> dict[str, Any]

Reorder a layer by name.

Provide exactly one of: - index: absolute target index - before: move before this layer name - after: move after this layer name

Source code in src/napari_mcp/server.py 
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async def reorder_layer(
    name: str,
    index: int | str | None = None,
    before: str | None = None,
    after: str | None = None,
) -> dict[str, Any]:
    """
    Reorder a layer by name.

    Provide exactly one of:
    - index: absolute target index
    - before: move before this layer name
    - after: move after this layer name
    """
    return await NapariMCPTools.reorder_layer(
        name=name, index=index, before=before, after=after
    )

set_active_layer async 

set_active_layer(name: str) -> dict[str, Any]

Set the selected/active layer by name.

Source code in src/napari_mcp/server.py 
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async def set_active_layer(name: str) -> dict[str, Any]:
    """Set the selected/active layer by name."""
    return await NapariMCPTools.set_active_layer(name)

reset_view async 

reset_view() -> dict[str, Any]

Reset the camera view to fit data.

Source code in src/napari_mcp/server.py 
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async def reset_view() -> dict[str, Any]:
    """Reset the camera view to fit data."""
    return await NapariMCPTools.reset_view()

set_camera async 

set_camera(center: list[float] | None = None, zoom: float | str | None = None, angle: float | str | None = None) -> dict[str, Any]

Set camera properties: center, zoom, and/or angle.

Source code in src/napari_mcp/server.py 
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async def set_camera(
    center: list[float] | None = None,
    zoom: float | str | None = None,
    angle: float | str | None = None,
) -> dict[str, Any]:
    """Set camera properties: center, zoom, and/or angle."""
    return await NapariMCPTools.set_camera(center=center, zoom=zoom, angle=angle)

set_ndisplay async 

set_ndisplay(ndisplay: int | str) -> dict[str, Any]

Set number of displayed dimensions (2 or 3).

Source code in src/napari_mcp/server.py 
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async def set_ndisplay(ndisplay: int | str) -> dict[str, Any]:
    """Set number of displayed dimensions (2 or 3)."""
    return await NapariMCPTools.set_ndisplay(ndisplay)

set_dims_current_step async 

set_dims_current_step(axis: int | str, value: int | str) -> dict[str, Any]

Set the current step (slider position) for a specific axis.

Source code in src/napari_mcp/server.py 
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async def set_dims_current_step(axis: int | str, value: int | str) -> dict[str, Any]:
    """Set the current step (slider position) for a specific axis."""
    return await NapariMCPTools.set_dims_current_step(axis, value)

set_grid async 

set_grid(enabled: bool | str = True) -> dict[str, Any]

Enable or disable grid view.

Source code in src/napari_mcp/server.py 
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async def set_grid(enabled: bool | str = True) -> dict[str, Any]:
    """Enable or disable grid view."""
    return await NapariMCPTools.set_grid(enabled)

screenshot async 

screenshot(canvas_only: bool | str = True) -> ImageContent

Take a screenshot of the napari canvas and return as base64.

Parameters:

Name Type Description Default
canvas_only bool

If True, only capture the canvas area.

 True

Returns:

Type Description
ImageContent

The screenshot image as an mcp.types.ImageContent object.
Source code in src/napari_mcp/server.py 
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async def screenshot(canvas_only: bool | str = True) -> ImageContent:
    """
    Take a screenshot of the napari canvas and return as base64.

    Parameters
    ----------
    canvas_only : bool, default=True
        If True, only capture the canvas area.

    Returns
    -------
    ImageContent
        The screenshot image as an mcp.types.ImageContent object.
    """
    return await NapariMCPTools.screenshot(canvas_only)

timelapse_screenshot async 

timelapse_screenshot(axis: int | str, slice_range: str, canvas_only: bool | str = True, interpolate_to_fit: bool = True) -> list[ImageContent]

Capture a series of screenshots while sweeping a dims axis.

Parameters:

Name Type Description Default
axis int

Dims axis index to sweep (e.g., temporal axis).

 required
slice_range str

Python-like slice string over step indices, e.g. "1:5", ":6", "::2". Defaults follow Python semantics with start=0, stop=nsteps, step=1.

 required
canvas_only bool

If True, only capture the canvas area.

 True
interpolate_to_fit bool

If True, interpolate the images to fit the total size cap of 1309246 bytes.

 False

Returns:

Type Description
list[ImageContent]

List of screenshots as mcp.types.ImageContent objects.
Source code in src/napari_mcp/server.py 
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async def timelapse_screenshot(
    axis: int | str,
    slice_range: str,
    canvas_only: bool | str = True,
    interpolate_to_fit: bool = True,
) -> list[ImageContent]:
    """
    Capture a series of screenshots while sweeping a dims axis.

    Parameters
    ----------
    axis : int
        Dims axis index to sweep (e.g., temporal axis).
    slice_range : str
        Python-like slice string over step indices, e.g. "1:5", ":6", "::2".
        Defaults follow Python semantics with start=0, stop=nsteps, step=1.
    canvas_only : bool, default=True
        If True, only capture the canvas area.
    interpolate_to_fit : bool, default=False
        If True, interpolate the images to fit the total size cap of 1309246 bytes.

    Returns
    -------
    list[ImageContent]
        List of screenshots as mcp.types.ImageContent objects.
    """
    return await NapariMCPTools.timelapse_screenshot(
        axis=axis,
        slice_range=slice_range,
        canvas_only=canvas_only,
        interpolate_to_fit=interpolate_to_fit,
    )

execute_code async 

execute_code(code: str, line_limit: int | str = 30) -> dict[str, Any]

Execute arbitrary Python code in the server's interpreter.

Similar to napari's console. The execution namespace persists across calls and includes 'viewer', 'napari', and 'np'.

Parameters:

Name Type Description Default
code str

Python code string. The value of the last expression (if any) is returned as 'result_repr'.

 required
line_limit int

Maximum number of output lines to return. Use -1 for unlimited output. Warning: Using -1 may consume a large number of tokens.

 30

Returns:

Type Description
dict

Dictionary with 'status', optional 'result_repr', 'stdout', 'stderr', and 'output_id' for retrieving full output if truncated.
Source code in src/napari_mcp/server.py 
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async def execute_code(code: str, line_limit: int | str = 30) -> dict[str, Any]:
    """
    Execute arbitrary Python code in the server's interpreter.

    Similar to napari's console. The execution namespace persists across calls
    and includes 'viewer', 'napari', and 'np'.

    Parameters
    ----------
    code : str
        Python code string. The value of the last expression (if any)
        is returned as 'result_repr'.
    line_limit : int, default=30
        Maximum number of output lines to return. Use -1 for unlimited output.
        Warning: Using -1 may consume a large number of tokens.

    Returns
    -------
    dict
        Dictionary with 'status', optional 'result_repr', 'stdout', 'stderr',
        and 'output_id' for retrieving full output if truncated.
    """
    return await NapariMCPTools.execute_code(code=code, line_limit=line_limit)

install_packages async 

install_packages(packages: list[str], upgrade: bool | None = False, no_deps: bool | None = False, index_url: str | None = None, extra_index_url: str | None = None, pre: bool | None = False, line_limit: int | str = 30, timeout: int = 240) -> dict[str, Any]

Install Python packages using pip.

Install packages into the currently running server environment.

Parameters:

Name Type Description Default
packages list of str

List of package specifiers (e.g., "scikit-image", "torch==2.3.1").

 required
upgrade bool

If True, pass --upgrade flag.

 False
no_deps bool

If True, pass --no-deps flag.

 False
index_url str

Custom index URL.

 None
extra_index_url str

Extra index URL.

 None
pre bool

Allow pre-releases (--pre flag).

 False
line_limit int

Maximum number of output lines to return. Use -1 for unlimited output. Warning: Using -1 may consume a large number of tokens.

 30
timeout int

Timeout for pip install in seconds.

 240

Returns:

Type Description
dict

Dictionary including status, returncode, stdout, stderr, command, and output_id for retrieving full output if truncated.
Source code in src/napari_mcp/server.py 
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async def install_packages(
    packages: list[str],
    upgrade: bool | None = False,
    no_deps: bool | None = False,
    index_url: str | None = None,
    extra_index_url: str | None = None,
    pre: bool | None = False,
    line_limit: int | str = 30,
    timeout: int = 240,
) -> dict[str, Any]:
    """
    Install Python packages using pip.

    Install packages into the currently running server environment.

    Parameters
    ----------
    packages : list of str
        List of package specifiers (e.g., "scikit-image", "torch==2.3.1").
    upgrade : bool, optional
        If True, pass --upgrade flag.
    no_deps : bool, optional
        If True, pass --no-deps flag.
    index_url : str, optional
        Custom index URL.
    extra_index_url : str, optional
        Extra index URL.
    pre : bool, optional
        Allow pre-releases (--pre flag).
    line_limit : int, default=30
        Maximum number of output lines to return. Use -1 for unlimited output.
        Warning: Using -1 may consume a large number of tokens.
    timeout : int, default=240
        Timeout for pip install in seconds.

    Returns
    -------
    dict
        Dictionary including status, returncode, stdout, stderr, command,
        and output_id for retrieving full output if truncated.
    """
    return await NapariMCPTools.install_packages(
        packages=packages,
        upgrade=upgrade,
        no_deps=no_deps,
        index_url=index_url,
        extra_index_url=extra_index_url,
        pre=pre,
        line_limit=line_limit,
        timeout=timeout,
    )

read_output async 

read_output(output_id: str, start: int | str = 0, end: int | str = -1) -> dict[str, Any]

Read stored tool output with optional line range.

Parameters:

Name Type Description Default
output_id str

Unique ID of the stored output.

 required
start int

Starting line number (0-indexed).

 0
end int

Ending line number (exclusive). If -1, read to end.

 -1

Returns:

Type Description
dict

Dictionary containing the requested output lines and metadata.
Source code in src/napari_mcp/server.py 
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async def read_output(
    output_id: str, start: int | str = 0, end: int | str = -1
) -> dict[str, Any]:
    """
    Read stored tool output with optional line range.

    Parameters
    ----------
    output_id : str
        Unique ID of the stored output.
    start : int, default=0
        Starting line number (0-indexed).
    end : int, default=-1
        Ending line number (exclusive). If -1, read to end.

    Returns
    -------
    dict
        Dictionary containing the requested output lines and metadata.
    """
    return await NapariMCPTools.read_output(output_id=output_id, start=start, end=end)

run 

run() -> None

Run the MCP server.

Source code in src/napari_mcp/server.py 
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@app.command()
def run() -> None:
    """Run the MCP server."""
    server.run()

install 

install() -> None

Install napari-mcp in various AI clients.

NOTE: This command is deprecated. Use 'napari-mcp-install' instead.

Source code in src/napari_mcp/server.py 
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@app.command()
def install() -> None:
    """
    Install napari-mcp in various AI clients.

    NOTE: This command is deprecated. Use 'napari-mcp-install' instead.
    """
    from rich.console import Console

    console = Console()
    console.print(
        "\n[bold yellow]⚠️  Deprecated Command[/bold yellow]\n",
        style="yellow",
    )
    console.print(
        "The 'napari-mcp install' command has been replaced by 'napari-mcp-install'.",
    )
    console.print("\n[bold green]To install napari-mcp:[/bold green]")
    console.print("  • Run: [bold cyan]napari-mcp-install --help[/bold cyan]")
    console.print(
        "  • Or: [bold cyan]napari-mcp-install claude-desktop[/bold cyan] (for example)\n"
    )

    console.print("[yellow]Please use 'napari-mcp-install' instead.[/yellow]\n")

    raise typer.Exit(1)

main 

main() -> None

Entry point that defaults to running the server.

Source code in src/napari_mcp/server.py 
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def main() -> None:
    """Entry point that defaults to running the server."""
    # If no arguments provided, default to running the server
    if len(sys.argv) == 1:
        server.run()
    else:
        app()

Implementation (NapariMCPTools class)

The actual implementation behind the MCP tools:

Implementation of Napari MCP tools (exactly matching server.py behavior).

Source code in src/napari_mcp/server.py 
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class NapariMCPTools:
    """Implementation of Napari MCP tools (exactly matching server.py behavior)."""

    @staticmethod
    async def detect_viewers() -> dict[str, Any]:
        """
        Detect available viewers (local and external).

        Returns
        -------
        dict
            Dictionary with information about available viewers
        """
        viewers: dict[str, Any] = {"local": None, "external": None}

        # Check for external viewer
        client, info = await _detect_external_viewer()
        if client and info is not None:
            viewers["external"] = {
                "available": True,
                "type": "napari_bridge",
                "port": info.get("bridge_port", _external_port),
                "viewer_info": info.get("viewer", {}),
            }
        else:
            viewers["external"] = {"available": False}

        # Check for local viewer
        global _viewer
        if _viewer is not None:
            viewers["local"] = {
                "available": True,
                "type": "singleton",
                "title": _viewer.title,
                "n_layers": len(_viewer.layers),
            }
        else:
            viewers["local"] = {
                "available": True,  # Can be created
                "type": "not_initialized",
            }

        return {
            "status": "ok",
            "viewers": viewers,
        }

    @staticmethod
    async def init_viewer(
        title: str | None = None,
        width: int | str | None = None,
        height: int | str | None = None,
        port: int | str | None = None,
    ) -> dict[str, Any]:
        """
        Create or return the napari viewer (local or external).

        Parameters
        ----------
        title : str, optional
            Optional window title (only for local viewer).
        width : int, optional
            Optional initial canvas width (only for local viewer).
        height : int, optional
            Optional initial canvas height (only for local viewer).
        port : int, optional
            If provided, attempt to connect to an external napari-mcp bridge on
            this port (default is taken from NAPARI_MCP_BRIDGE_PORT or 9999).

        Returns
        -------
        dict
            Dictionary containing status, viewer type, and layer info.
        """
        # Allow overriding the external port per-call
        global _external_port
        if port is not None:
            try:
                _external_port = int(port)
            except Exception:
                logger.error("Invalid port: {port}")
                _external_port = _external_port

        async with _viewer_lock:
            # Try external viewer first; fall back to local
            try:
                return await NapariMCPTools._external_session_information(
                    _external_port
                )
            except Exception:
                # No external viewer; continue to local viewer
                pass

            # Use local viewer
            v = _ensure_viewer()
            if title:
                v.title = title
            if width or height:
                w = (
                    int(width)
                    if width is not None
                    else v.window.qt_viewer.canvas.size().width()
                )
                h = (
                    int(height)
                    if height is not None
                    else v.window.qt_viewer.canvas.size().height()
                )
                v.window.qt_viewer.canvas.native.resize(w, h)
            # Always ensure GUI pump is running for local viewer (backwards-incompatible change)
            global _qt_pump_task
            app = _ensure_qt_app()
            with contextlib.suppress(Exception):
                app.setQuitOnLastWindowClosed(False)
            _connect_window_destroyed_signal(v)

            # Best-effort to show window without forcing focus (safer for tests/headless)
            try:
                qt_win = v.window._qt_window  # type: ignore[attr-defined]
                qt_win.show()
            except Exception:
                pass

            if _qt_pump_task is None or _qt_pump_task.done():
                loop = asyncio.get_running_loop()
                _qt_pump_task = loop.create_task(_qt_event_pump())

            _process_events()
            return {
                "status": "ok",
                "viewer_type": "local",
                "title": v.title,
                "layers": [lyr.name for lyr in v.layers],
            }

    @staticmethod
    async def _external_session_information(_external_port: int) -> dict[str, Any]:
        """Get session information from the external viewer."""
        test_client = Client(f"http://localhost:{_external_port}/mcp")
        async with test_client:
            result = await test_client.call_tool("session_information")
            if hasattr(result, "content"):
                content = result.content
                if isinstance(content, list) and len(content) > 0:
                    import json

                    info = (
                        content[0].text
                        if hasattr(content[0], "text")
                        else str(content[0])
                    )
                    info_dict = json.loads(info) if isinstance(info, str) else info
                    if info_dict.get("session_type") == "napari_bridge_session":
                        return {
                            "status": "ok",
                            "viewer_type": "external",
                            "title": info_dict.get("viewer", {}).get(
                                "title", "External Viewer"
                            ),
                            "layers": info_dict.get("viewer", {}).get(
                                "layer_names", []
                            ),
                            "port": info_dict.get("bridge_port", _external_port),
                        }

        return {
            "status": "error",
            "message": "Failed to get session information from external viewer",
        }

    @staticmethod
    async def close_viewer() -> dict[str, Any]:
        """
        Close the viewer window and clear all layers.

        Returns
        -------
        dict
            Dictionary with status: 'closed' if viewer existed, 'no_viewer' if none.
        """
        async with _viewer_lock:
            global _viewer, _qt_pump_task
            if _viewer is not None:
                _viewer.close()
                _viewer = None
                # Stop GUI pump when closing viewer
                if _qt_pump_task is not None and not _qt_pump_task.done():
                    _qt_pump_task.cancel()
                    with contextlib.suppress(asyncio.CancelledError):
                        await _qt_pump_task
                _qt_pump_task = None
                _process_events()
                return {"status": "closed"}
            return {"status": "no_viewer"}

    @staticmethod
    async def session_information() -> dict[str, Any]:
        """
        Get comprehensive information about the current napari session.

        Returns
        -------
        dict
            Comprehensive session information including viewer state, system info,
            and environment details.
        """
        import os
        import platform

        async with _viewer_lock:
            global _viewer, _qt_pump_task, _exec_globals

            try:
                return await NapariMCPTools._external_session_information(
                    _external_port
                )
            except Exception:
                # No external viewer; continue to local viewer
                pass

            # Use local viewer

            # Check if viewer exists
            viewer_exists = _viewer is not None
            if not viewer_exists:
                return {
                    "status": "ok",
                    "session_type": "napari_mcp_standalone_session",
                    "timestamp": str(np.datetime64("now")),
                    "viewer": None,
                    "message": "No viewer currently initialized. Call init_viewer() first.",
                }

            v = _viewer
            assert v is not None  # We already checked this above

            # Viewer information
            viewer_info = {
                "title": v.title,
                "viewer_id": id(v),
                "n_layers": len(v.layers),
                "layer_names": [layer.name for layer in v.layers],
                "selected_layers": [layer.name for layer in v.layers.selection],
                "current_step": dict(enumerate(v.dims.current_step))
                if hasattr(v.dims, "current_step")
                else {},
                "ndisplay": v.dims.ndisplay,
                "camera_center": list(v.camera.center),
                "camera_zoom": float(v.camera.zoom),
                "camera_angles": list(v.camera.angles) if v.camera.angles else [],
                "grid_enabled": v.grid.enabled,
            }

            # System information
            system_info = {
                "python_version": sys.version,
                "platform": platform.platform(),
                "napari_version": getattr(napari, "__version__", "unknown"),
                "process_id": os.getpid(),
                "working_directory": os.getcwd(),
            }

            # Session status
            gui_running = _qt_pump_task is not None and not _qt_pump_task.done()
            session_info = {
                "server_type": "napari_mcp_standalone",
                "viewer_instance": f"<napari.Viewer at {hex(id(v))}>",
                "gui_pump_running": gui_running,
                "execution_namespace_vars": list(_exec_globals.keys()),
                "qt_app_available": _qt_app is not None,
            }

            # Layer details
            layer_details = []
            for layer in v.layers:
                layer_detail = {
                    "name": layer.name,
                    "type": layer.__class__.__name__,
                    "visible": _parse_bool(getattr(layer, "visible", True)),
                    "opacity": float(getattr(layer, "opacity", 1.0)),
                    "blending": getattr(layer, "blending", None),
                    "data_shape": list(layer.data.shape)
                    if hasattr(layer, "data") and hasattr(layer.data, "shape")
                    else None,
                    "data_dtype": str(layer.data.dtype)
                    if hasattr(layer, "data") and hasattr(layer.data, "dtype")
                    else None,
                    "layer_id": id(layer),
                }

                # Add layer-specific properties
                if hasattr(layer, "colormap"):
                    layer_detail["colormap"] = getattr(
                        layer.colormap, "name", str(layer.colormap)
                    )
                if hasattr(layer, "contrast_limits"):
                    try:
                        cl = layer.contrast_limits
                        layer_detail["contrast_limits"] = [float(cl[0]), float(cl[1])]
                    except Exception:
                        pass
                if hasattr(layer, "gamma"):
                    layer_detail["gamma"] = float(getattr(layer, "gamma", 1.0))

                layer_details.append(layer_detail)

            return {
                "status": "ok",
                "session_type": "napari_mcp_standalone_session",
                "timestamp": str(np.datetime64("now")),
                "viewer": viewer_info,
                "system": system_info,
                "session": session_info,
                "layers": layer_details,
            }

    @staticmethod
    async def list_layers() -> list[dict[str, Any]]:
        """Return a list of layers with key properties."""
        # Try to proxy to external viewer first
        proxy_result = await _proxy_to_external("list_layers")
        if proxy_result is not None:
            # Ensure the result is the expected list format
            if isinstance(proxy_result, list):
                return proxy_result
            elif isinstance(proxy_result, dict) and "content" in proxy_result:
                content = proxy_result["content"]
                if isinstance(content, list):
                    return content
            return []

        # Local execution
        async with _viewer_lock:

            def _build():
                v = _ensure_viewer()
                result: list[dict[str, Any]] = []  # type: ignore
                for lyr in v.layers:
                    entry = {
                        "name": lyr.name,
                        "type": lyr.__class__.__name__,
                        "visible": _parse_bool(getattr(lyr, "visible", True)),
                        "opacity": float(getattr(lyr, "opacity", 1.0)),
                        "blending": getattr(lyr, "blending", None),
                    }
                    if (
                        hasattr(lyr, "colormap")
                        and getattr(lyr, "colormap", None) is not None
                    ):
                        entry["colormap"] = getattr(lyr.colormap, "name", None) or str(
                            lyr.colormap
                        )
                    if (
                        hasattr(lyr, "contrast_limits")
                        and getattr(lyr, "contrast_limits", None) is not None
                    ):
                        try:
                            cl = list(lyr.contrast_limits)
                            entry["contrast_limits"] = [float(cl[0]), float(cl[1])]
                        except Exception:
                            pass
                    result.append(entry)
                return result

            return _gui_execute(_build)

    @staticmethod
    async def add_image(
        path: str,
        name: str | None = None,
        colormap: str | None = None,
        blending: str | None = None,
        channel_axis: int | str | None = None,
    ) -> dict[str, Any]:
        """
        Add an image layer from a file path.

        Parameters
        ----------
        path : str
            Path to an image readable by imageio (e.g., PNG, TIFF, OME-TIFF).
        name : str, optional
            Layer name. If None, uses filename.
        colormap : str, optional
            Napari colormap name (e.g., 'gray', 'magma').
        blending : str, optional
            Blending mode (e.g., 'translucent').
        channel_axis : int, optional
            If provided, interpret that axis as channels.

        Returns
        -------
        dict
            Dictionary containing status, layer name, and image shape.
        """
        # Try to proxy to external viewer first
        params: dict[str, Any] = {"path": path}
        if name:
            params["name"] = name
        if colormap:
            params["colormap"] = colormap
        if blending:
            params["blending"] = blending
        if channel_axis is not None:
            params["channel_axis"] = int(channel_axis)

        result = await _proxy_to_external("add_image", params)
        if result is not None:
            return result

        # Local execution
        import imageio.v3 as iio

        async with _viewer_lock:
            data = iio.imread(path)

            def _add():
                v = _ensure_viewer()
                layer = v.add_image(
                    data,
                    name=name,
                    colormap=colormap,
                    blending=blending,
                    channel_axis=channel_axis,
                )
                _process_events()
                return {
                    "status": "ok",
                    "name": layer.name,
                    "shape": list(np.shape(data)),
                }

            return _gui_execute(_add)

    @staticmethod
    async def add_labels(path: str, name: str | None = None) -> dict[str, Any]:
        """Add a labels layer from a file path (e.g., PNG/TIFF with integer labels)."""
        import imageio.v3 as iio

        async with _viewer_lock:
            try:
                from pathlib import Path

                def _add():
                    v = _ensure_viewer()
                    p = Path(path).expanduser().resolve(strict=False)
                    data = iio.imread(str(p))
                    layer = v.add_labels(data, name=name)
                    _process_events()
                    return {
                        "status": "ok",
                        "name": layer.name,
                        "shape": list(np.shape(data)),
                    }

                return _gui_execute(_add)
            except Exception as e:
                return {
                    "status": "error",
                    "message": f"Failed to add labels from '{path}': {e}",
                }

    @staticmethod
    async def add_points(
        points: list[list[float]], name: str | None = None, size: float | str = 10.0
    ) -> dict[str, Any]:
        """
        Add a points layer.

        - points: List of [y, x] or [z, y, x] coordinates
        - name: Optional layer name
        - size: Point size in pixels
        """
        async with _viewer_lock:

            def _add():
                v = _ensure_viewer()
                arr = np.asarray(points, dtype=float)
                layer = v.add_points(arr, name=name, size=float(size))
                _process_events()
                return {
                    "status": "ok",
                    "name": layer.name,
                    "n_points": int(arr.shape[0]),
                }

            return _gui_execute(_add)

    @staticmethod
    async def remove_layer(name: str) -> dict[str, Any]:
        """Remove a layer by name."""
        async with _viewer_lock:

            def _remove():
                v = _ensure_viewer()
                if name in v.layers:
                    v.layers.remove(name)
                    _process_events()
                    return {"status": "removed", "name": name}
                return {"status": "not_found", "name": name}

            return _gui_execute(_remove)

    # Removed: rename_layer (use set_layer_properties with new_name instead)

    @staticmethod
    async def set_layer_properties(
        name: str,
        visible: bool | None = None,
        opacity: float | None = None,
        colormap: str | None = None,
        blending: str | None = None,
        contrast_limits: list[float] | None = None,
        gamma: float | str | None = None,
        new_name: str | None = None,
    ) -> dict[str, Any]:
        """Set common properties on a layer by name."""
        async with _viewer_lock:

            def _set():
                v = _ensure_viewer()
                if name not in v.layers:
                    return {"status": "not_found", "name": name}
                lyr = v.layers[name]
                if visible is not None and hasattr(lyr, "visible"):
                    lyr.visible = _parse_bool(visible)
                if opacity is not None and hasattr(lyr, "opacity"):
                    lyr.opacity = float(opacity)
                if colormap is not None and hasattr(lyr, "colormap"):
                    lyr.colormap = colormap
                if blending is not None and hasattr(lyr, "blending"):
                    lyr.blending = blending
                if contrast_limits is not None and hasattr(lyr, "contrast_limits"):
                    with contextlib.suppress(Exception):
                        lyr.contrast_limits = [
                            float(contrast_limits[0]),
                            float(contrast_limits[1]),
                        ]
                if gamma is not None and hasattr(lyr, "gamma"):
                    lyr.gamma = float(gamma)
                if new_name is not None:
                    lyr.name = new_name
                _process_events()
                return {"status": "ok", "name": lyr.name}

            return _gui_execute(_set)

    @staticmethod
    async def reorder_layer(
        name: str,
        index: int | str | None = None,
        before: str | None = None,
        after: str | None = None,
    ) -> dict[str, Any]:
        """
        Reorder a layer by name.

        Provide exactly one of:
        - index: absolute target index
        - before: move before this layer name
        - after: move after this layer name
        """
        async with _viewer_lock:

            def _reorder():
                v = _ensure_viewer()
                if name not in v.layers:
                    return {"status": "not_found", "name": name}
                if sum(x is not None for x in (index, before, after)) != 1:
                    return {
                        "status": "error",
                        "message": "Provide exactly one of index, before, or after",
                    }
                cur = v.layers.index(name)
                target = cur
                if index is not None:
                    target = max(0, min(int(index), len(v.layers) - 1))
                elif before is not None:
                    if before not in v.layers:
                        return {"status": "not_found", "name": before}
                    target = v.layers.index(before)
                elif after is not None:
                    if after not in v.layers:
                        return {"status": "not_found", "name": after}
                    target = v.layers.index(after) + 1
                if target != cur:
                    v.layers.move(cur, target)
                _process_events()
                return {"status": "ok", "name": name, "index": v.layers.index(name)}

            return _gui_execute(_reorder)

    @staticmethod
    async def set_active_layer(name: str) -> dict[str, Any]:
        """Set the selected/active layer by name."""
        async with _viewer_lock:

            def _set_active():
                v = _ensure_viewer()
                if name not in v.layers:
                    return {"status": "not_found", "name": name}
                v.layers.selection = {v.layers[name]}
                _process_events()
                return {"status": "ok", "active": name}

            return _gui_execute(_set_active)

    @staticmethod
    async def reset_view() -> dict[str, Any]:
        """Reset the camera view to fit data."""
        async with _viewer_lock:

            def _reset():
                v = _ensure_viewer()
                v.reset_view()
                _process_events()
                return {"status": "ok"}

            return _gui_execute(_reset)

    # Removed: set_zoom (use set_camera with zoom instead)

    @staticmethod
    async def set_camera(
        center: list[float] | None = None,
        zoom: float | str | None = None,
        angle: float | str | None = None,
    ) -> dict[str, Any]:
        """Set camera properties: center, zoom, and/or angle."""
        async with _viewer_lock:

            def _set_cam():
                v = _ensure_viewer()
                if center is not None:
                    v.camera.center = list(map(float, center))
                if zoom is not None:
                    v.camera.zoom = float(zoom)
                if angle is not None:
                    v.camera.angles = (float(angle),)
                _process_events()
                return {
                    "status": "ok",
                    "center": list(map(float, v.camera.center)),
                    "zoom": float(v.camera.zoom),
                }

            return _gui_execute(_set_cam)

    @staticmethod
    async def set_ndisplay(ndisplay: int | str) -> dict[str, Any]:
        """Set number of displayed dimensions (2 or 3)."""
        async with _viewer_lock:

            def _set():
                v = _ensure_viewer()
                v.dims.ndisplay = int(ndisplay)
                _process_events()
                return {"status": "ok", "ndisplay": int(v.dims.ndisplay)}

            return _gui_execute(_set)

    @staticmethod
    async def set_dims_current_step(
        axis: int | str, value: int | str
    ) -> dict[str, Any]:
        """Set the current step (slider position) for a specific axis."""
        async with _viewer_lock:

            def _set():
                v = _ensure_viewer()
                v.dims.set_current_step(int(axis), int(value))
                _process_events()
                return {"status": "ok", "axis": int(axis), "value": int(value)}

            return _gui_execute(_set)

    @staticmethod
    async def set_grid(enabled: bool | str = True) -> dict[str, Any]:
        """Enable or disable grid view."""
        async with _viewer_lock:

            def _set():
                v = _ensure_viewer()
                v.grid.enabled = _parse_bool(enabled)
                _process_events()
                return {"status": "ok", "grid": _parse_bool(v.grid.enabled)}

            return _gui_execute(_set)

    @staticmethod
    async def screenshot(canvas_only: bool | str = True) -> ImageContent:
        """
        Take a screenshot of the napari canvas and return as base64.

        Parameters
        ----------
        canvas_only : bool, default=True
            If True, only capture the canvas area.

        Returns
        -------
        ImageContent
            The screenshot image as an mcp.types.ImageContent object.
        """
        # Try to proxy to external viewer first
        result = await _proxy_to_external("screenshot", {"canvas_only": canvas_only})
        if result is not None:
            return result

        # Local execution
        async with _viewer_lock:

            def _shot():
                v = _ensure_viewer()
                _process_events(3)
                arr = v.screenshot(canvas_only=canvas_only)
                if not isinstance(arr, np.ndarray):
                    arr = np.asarray(arr)
                if arr.dtype != np.uint8:
                    arr = arr.astype(np.uint8, copy=False)
                img = Image.fromarray(arr)
                buf = BytesIO()
                img.save(buf, format="PNG")
                enc = buf.getvalue()
                return fastmcp.utilities.types.Image(
                    data=enc, format="png"
                ).to_image_content()

            return _gui_execute(_shot)

    @staticmethod
    async def timelapse_screenshot(
        axis: int | str,
        slice_range: str,
        canvas_only: bool | str = True,
        interpolate_to_fit: bool = True,
    ) -> list[ImageContent]:
        """
        Capture a series of screenshots while sweeping a dims axis.

        Parameters
        ----------
        axis : int
            Dims axis index to sweep (e.g., temporal axis).
        slice_range : str
            Python-like slice string over step indices, e.g. "1:5", ":6", "::2".
            Defaults follow Python semantics with start=0, stop=nsteps, step=1.
        canvas_only : bool, default=True
            If True, only capture the canvas area.
        interpolate_to_fit : bool, default=False
            If True, interpolate the images to fit the total size cap of 1309246 bytes.

        Returns
        -------
        list[ImageContent]
            List of screenshots as mcp.types.ImageContent objects.
        """
        max_total_base64_bytes = 1309246 if interpolate_to_fit else None

        # Try to proxy to external viewer first
        result = await _proxy_to_external(
            "timelapse_screenshot",
            {
                "axis": axis,
                "slice_range": slice_range,
                "canvas_only": canvas_only,
                "interpolate_to_fit": interpolate_to_fit,
            },
        )
        if result is not None:
            return result  # type: ignore[return-value]

        def _parse_slice(spec: str, length: int) -> list[int]:
            # Normalize
            s = (spec or "").strip()
            # Single integer
            if s and ":" not in s:
                try:
                    idx = int(s)
                except Exception as err:
                    raise ValueError(f"Invalid slice range: {spec!r}") from err
                if idx < 0:
                    idx += length
                if not (0 <= idx < length):
                    raise ValueError(
                        f"Index out of bounds for axis with {length} steps: {idx}"
                    )
                return [idx]

            # Slice form start:stop:step
            parts = s.split(":")
            if len(parts) > 3:
                raise ValueError(f"Invalid slice range: {spec!r}")
            start_s, stop_s, step_s = (parts + [""] * 3)[:3]

            def _to_int_or_none(val: str) -> int | None:
                v = val.strip()
                if v == "":
                    return None
                return int(v)

            start = _to_int_or_none(start_s)
            stop = _to_int_or_none(stop_s)
            step = _to_int_or_none(step_s) or 1
            if step == 0:
                raise ValueError("slice step cannot be 0")

            # Handle negatives like Python
            if start is None:
                start = 0 if step > 0 else length - 1
            if stop is None:
                stop = length if step > 0 else -1
            if start < 0:
                start += length
            if stop < 0:
                stop += length

            # Clamp to valid iteration range similar to range() behavior
            rng = range(start, stop, step)
            indices = [i for i in rng if 0 <= i < length]
            return indices

        # Local execution
        async with _viewer_lock:

            def _run_series():
                v = _ensure_viewer()

                # Determine number of steps along axis
                try:
                    nsteps_tuple = getattr(v.dims, "nsteps", None)
                    if nsteps_tuple is None:
                        # Fallback: infer from current_step length and a conservative stop
                        # We cannot reliably infer total steps without dims.nsteps; require it
                        raise AttributeError
                    total = int(nsteps_tuple[int(axis)])
                except Exception:
                    # Best effort via bounds from layers; may be approximate
                    try:
                        total = max(
                            int(getattr(lyr.data, "shape", [1])[(int(axis))])
                            if int(axis) < getattr(lyr.data, "ndim", 0)
                            else 1
                            for lyr in v.layers
                        )
                    except Exception:
                        total = 0

                if total <= 0:
                    raise RuntimeError(
                        "Unable to determine number of steps for the given axis"
                    )

                indices = _parse_slice(slice_range, total)
                if not indices:
                    return []

                # Take a sample at the first index to estimate size
                v.dims.set_current_step(int(axis), int(indices[0]))
                _process_events(2)
                sample_arr = v.screenshot(canvas_only=canvas_only)
                if not isinstance(sample_arr, np.ndarray):
                    sample_arr = np.asarray(sample_arr)
                if sample_arr.dtype != np.uint8:
                    sample_arr = sample_arr.astype(np.uint8, copy=False)
                sample_img = Image.fromarray(sample_arr)
                sbuf = BytesIO()
                sample_img.save(sbuf, format="PNG")
                sample_png = sbuf.getvalue()
                sample_b64_len = ((len(sample_png) + 2) // 3) * 4

                # Ask user whether to downsample if estimated total exceeds cap
                downsample_factor = 1.0
                if (
                    max_total_base64_bytes is not None
                    and sample_b64_len * len(indices) > max_total_base64_bytes
                ):
                    est_factor = math.sqrt(
                        max_total_base64_bytes
                        / float(max(1, sample_b64_len * len(indices)))
                    )
                    downsample_factor = max(0.05, min(1.0, est_factor))

                images: list[ImageContent] = []  # type: ignore
                total_b64_len = 0
                for idx in indices:
                    # Move slider
                    v.dims.set_current_step(int(axis), int(idx))
                    _process_events(2)

                    # Capture
                    arr = v.screenshot(canvas_only=canvas_only)
                    if not isinstance(arr, np.ndarray):
                        arr = np.asarray(arr)
                    if arr.dtype != np.uint8:
                        arr = arr.astype(np.uint8, copy=False)

                    img = Image.fromarray(arr)
                    if downsample_factor < 1.0:
                        new_w = max(1, int(img.width * downsample_factor))
                        new_h = max(1, int(img.height * downsample_factor))
                        if new_w != img.width or new_h != img.height:
                            img = img.resize((new_w, new_h), resample=Image.BILINEAR)
                    buf = BytesIO()
                    img.save(buf, format="PNG")
                    enc = buf.getvalue()
                    b64_len = ((len(enc) + 2) // 3) * 4
                    if (
                        max_total_base64_bytes is not None
                        and total_b64_len + b64_len > max_total_base64_bytes
                    ):
                        break
                    total_b64_len += b64_len
                    images.append(
                        fastmcp.utilities.types.Image(
                            data=enc, format="png"
                        ).to_image_content()
                    )

                return images

            return _gui_execute(_run_series)

    @staticmethod
    async def execute_code(code: str, line_limit: int | str = 30) -> dict[str, Any]:
        """
        Execute arbitrary Python code in the server's interpreter.

        Similar to napari's console. The execution namespace persists across calls
        and includes 'viewer', 'napari', and 'np'.

        Parameters
        ----------
        code : str
            Python code string. The value of the last expression (if any)
            is returned as 'result_repr'.
        line_limit : int, default=30
            Maximum number of output lines to return. Use -1 for unlimited output.
            Warning: Using -1 may consume a large number of tokens.

        Returns
        -------
        dict
            Dictionary with 'status', optional 'result_repr', 'stdout', 'stderr',
            and 'output_id' for retrieving full output if truncated.
        """
        # Try to proxy to external viewer first
        result = await _proxy_to_external("execute_code", {"code": code})
        if result is not None:
            return result

        # Local execution
        async with _viewer_lock:
            v = _ensure_viewer()
            _exec_globals.setdefault("__builtins__", __builtins__)  # type: ignore[assignment]
            _exec_globals["viewer"] = v
            napari_mod = napari
            if napari_mod is not None:
                _exec_globals.setdefault("napari", napari_mod)
            _exec_globals.setdefault("np", np)

            stdout_buf = StringIO()
            stderr_buf = StringIO()
            result_repr: str | None = None
            try:
                # Capture stdout/stderr during execution
                with (
                    contextlib.redirect_stdout(stdout_buf),
                    contextlib.redirect_stderr(stderr_buf),
                ):
                    # Try to evaluate last expression if present
                    parsed = ast.parse(code, mode="exec")
                    if parsed.body and isinstance(parsed.body[-1], ast.Expr):
                        # Execute all but last, then eval last expression to
                        # capture a result
                        if len(parsed.body) > 1:
                            exec_ast = ast.Module(
                                body=parsed.body[:-1], type_ignores=[]
                            )
                            exec(
                                compile(exec_ast, "<mcp-exec>", "exec"),
                                _exec_globals,
                                _exec_globals,
                            )
                        last_expr = ast.Expression(body=parsed.body[-1].value)
                        value = eval(
                            compile(last_expr, "<mcp-eval>", "eval"),
                            _exec_globals,
                            _exec_globals,
                        )
                        result_repr = repr(value)
                    else:
                        # Pure statements
                        exec(
                            compile(parsed, "<mcp-exec>", "exec"),
                            _exec_globals,
                            _exec_globals,
                        )
                _process_events(2)

                # Get full output
                stdout_full = stdout_buf.getvalue()
                stderr_full = stderr_buf.getvalue()

                # Store full output and get ID
                output_id = await _store_output(
                    tool_name="execute_code",
                    stdout=stdout_full,
                    stderr=stderr_full,
                    result_repr=result_repr,
                    code=code,
                )

                # Prepare response with line limiting
                response = {
                    "status": "ok",
                    "output_id": output_id,
                    **({"result_repr": result_repr} if result_repr is not None else {}),
                }

                # Add warning for unlimited output
                if line_limit == -1:
                    response["warning"] = (
                        "Unlimited output requested. This may consume a large number "
                        "of tokens. Consider using read_output for large outputs."
                    )
                    response["stdout"] = stdout_full
                    response["stderr"] = stderr_full
                else:
                    # Truncate stdout and stderr
                    stdout_truncated, stdout_was_truncated = _truncate_output(
                        stdout_full, int(line_limit)
                    )
                    stderr_truncated, stderr_was_truncated = _truncate_output(
                        stderr_full, int(line_limit)
                    )

                    response["stdout"] = stdout_truncated
                    response["stderr"] = stderr_truncated

                    if stdout_was_truncated or stderr_was_truncated:
                        response["truncated"] = True  # type: ignore
                        response["message"] = (
                            f"Output truncated to {line_limit} lines. "
                            f"Use read_output('{output_id}') to retrieve full output."
                        )

                return response
            except Exception as e:
                _process_events(1)
                tb = traceback.format_exc()

                # Get full output including traceback
                stdout_full = stdout_buf.getvalue()
                stderr_full = stderr_buf.getvalue() + tb

                # Store full output and get ID
                output_id = await _store_output(
                    tool_name="execute_code",
                    stdout=stdout_full,
                    stderr=stderr_full,
                    code=code,
                    error=True,
                )

                # Prepare error response with line limiting
                response = {
                    "status": "error",
                    "output_id": output_id,
                }

                # Add warning for unlimited output
                if line_limit == -1:
                    response["warning"] = (
                        "Unlimited output requested. This may consume a large number "
                        "of tokens. Consider using read_output for large outputs."
                    )
                    response["stdout"] = stdout_full
                    response["stderr"] = stderr_full
                else:
                    # Truncate stdout and stderr
                    stdout_truncated, stdout_was_truncated = _truncate_output(
                        stdout_full, int(line_limit)
                    )
                    stderr_truncated, stderr_was_truncated = _truncate_output(
                        stderr_full, int(line_limit)
                    )

                    response["stdout"] = stdout_truncated
                    # Ensure exception summary is present even when truncated
                    error_summary = f"{type(e).__name__}: {e}"
                    if error_summary not in stderr_truncated:
                        # Append a concise summary line so callers can see the error type
                        if stderr_truncated and not stderr_truncated.endswith("\n"):
                            stderr_truncated += "\n"
                        stderr_truncated += error_summary + "\n"
                    response["stderr"] = stderr_truncated

                    if stdout_was_truncated or stderr_was_truncated:
                        response["truncated"] = True  # type: ignore
                        response["message"] = (
                            f"Output truncated to {line_limit} lines. "
                            f"Use read_output('{output_id}') to retrieve full output."
                        )

                return response

    @staticmethod
    async def install_packages(
        packages: list[str],
        upgrade: bool | None = False,
        no_deps: bool | None = False,
        index_url: str | None = None,
        extra_index_url: str | None = None,
        pre: bool | None = False,
        line_limit: int | str = 30,
        timeout: int = 240,
    ) -> dict[str, Any]:
        """
        Install Python packages using pip.

        Install packages into the currently running server environment.

        Parameters
        ----------
        packages : list of str
            List of package specifiers (e.g., "scikit-image", "torch==2.3.1").
        upgrade : bool, optional
            If True, pass --upgrade flag.
        no_deps : bool, optional
            If True, pass --no-deps flag.
        index_url : str, optional
            Custom index URL.
        extra_index_url : str, optional
            Extra index URL.
        pre : bool, optional
            Allow pre-releases (--pre flag).
        line_limit : int, default=30
            Maximum number of output lines to return. Use -1 for unlimited output.
            Warning: Using -1 may consume a large number of tokens.
        timeout : int, default=240
            Timeout for pip install in seconds.

        Returns
        -------
        dict
            Dictionary including status, returncode, stdout, stderr, command,
            and output_id for retrieving full output if truncated.
        """
        # Try to proxy to external viewer first
        result = await _proxy_to_external(
            "install_packages",
            {
                "packages": packages,
                "upgrade": upgrade,
                "no_deps": no_deps,
                "index_url": index_url,
                "extra_index_url": extra_index_url,
                "pre": pre,
                "line_limit": line_limit,
                "timeout": timeout,
            },
        )
        if result is not None:
            return result

        if not packages or not isinstance(packages, list):
            return {
                "status": "error",
                "message": "Parameter 'packages' must be a non-empty list of package names",
            }

        cmd: list[str] = [
            sys.executable,
            "-m",
            "pip",
            "install",
            "--no-input",
            "--disable-pip-version-check",
        ]
        if upgrade:
            cmd.append("--upgrade")
        if no_deps:
            cmd.append("--no-deps")
        if pre:
            cmd.append("--pre")
        if index_url:
            cmd.extend(["--index-url", index_url])
        if extra_index_url:
            cmd.extend(["--extra-index-url", extra_index_url])
        cmd.extend(packages)

        # Run pip as a subprocess without blocking the event loop
        proc = await asyncio.create_subprocess_exec(
            *cmd,
            stdout=asyncio.subprocess.PIPE,
            stderr=asyncio.subprocess.PIPE,
        )
        try:
            stdout_b, stderr_b = await asyncio.wait_for(
                proc.communicate(), timeout=timeout
            )
        except asyncio.TimeoutError:
            with contextlib.suppress(ProcessLookupError):
                proc.kill()
            stdout_b, stderr_b = b"", f"pip install timed out after {timeout}s".encode()
        stdout = stdout_b.decode(errors="replace")
        stderr = stderr_b.decode(errors="replace")

        status = "ok" if proc.returncode == 0 else "error"
        command_str = " ".join(shlex.quote(part) for part in cmd)

        # Store full output and get ID
        output_id = await _store_output(
            tool_name="install_packages",
            stdout=stdout,
            stderr=stderr,
            packages=packages,
            command=command_str,
            returncode=proc.returncode,
        )

        # Prepare response with line limiting
        response = {
            "status": status,
            "returncode": proc.returncode if proc.returncode is not None else -1,
            "command": command_str,
            "output_id": output_id,
        }

        # Add warning for unlimited output
        if line_limit == -1:
            response["warning"] = (
                "Unlimited output requested. This may consume a large number "
                "of tokens. Consider using read_output for large outputs."
            )
            response["stdout"] = stdout
            response["stderr"] = stderr
        else:
            # Truncate stdout and stderr
            stdout_truncated, stdout_was_truncated = _truncate_output(
                stdout, int(line_limit)
            )
            stderr_truncated, stderr_was_truncated = _truncate_output(
                stderr, int(line_limit)
            )

            response["stdout"] = stdout_truncated
            response["stderr"] = stderr_truncated

            if stdout_was_truncated or stderr_was_truncated:
                response["truncated"] = True
                response["message"] = (
                    f"Output truncated to {line_limit} lines. "
                    f"Use read_output('{output_id}') to retrieve full output."
                )

        return response

    @staticmethod
    async def read_output(
        output_id: str, start: int | str = 0, end: int | str = -1
    ) -> dict[str, Any]:
        """
        Read stored tool output with optional line range.

        Parameters
        ----------
        output_id : str
            Unique ID of the stored output.
        start : int, default=0
            Starting line number (0-indexed).
        end : int, default=-1
            Ending line number (exclusive). If -1, read to end.

        Returns
        -------
        dict
            Dictionary containing the requested output lines and metadata.
        """
        async with _output_storage_lock:
            if output_id not in _output_storage:
                return {
                    "status": "error",
                    "message": f"Output ID '{output_id}' not found",
                }

            stored_output = _output_storage[output_id]

            # Combine stdout and stderr for line-based access
            full_output = ""
            if stored_output.get("stdout"):
                full_output = stored_output["stdout"]
            if stored_output.get("stderr"):
                stderr_text = stored_output["stderr"]
                if (
                    full_output
                    and not full_output.endswith("\n")
                    and not stderr_text.startswith("\n")
                ):
                    full_output += "\n"
                full_output += stderr_text

            # Normalize and clamp range inputs
            try:
                start = int(start)
            except Exception:
                start = 0
            try:
                end = int(end)
            except Exception:
                end = -1

            start = max(0, start)

            lines = full_output.splitlines(keepends=True)
            total_lines = len(lines)

            # Handle line range
            end = total_lines if end == -1 else min(total_lines, end)

            selected_lines = [] if start >= total_lines else lines[start:end]

            return {
                "status": "ok",
                "output_id": output_id,
                "tool_name": stored_output["tool_name"],
                "timestamp": stored_output["timestamp"],
                "lines": selected_lines,
                "line_range": {"start": start, "end": min(end, total_lines)},
                "total_lines": total_lines,
                "result_repr": stored_output.get("result_repr"),
            }

Functions

detect_viewers async staticmethod 

detect_viewers() -> dict[str, Any]

Detect available viewers (local and external).

Returns:

Type Description
dict

Dictionary with information about available viewers
Source code in src/napari_mcp/server.py 
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@staticmethod
async def detect_viewers() -> dict[str, Any]:
    """
    Detect available viewers (local and external).

    Returns
    -------
    dict
        Dictionary with information about available viewers
    """
    viewers: dict[str, Any] = {"local": None, "external": None}

    # Check for external viewer
    client, info = await _detect_external_viewer()
    if client and info is not None:
        viewers["external"] = {
            "available": True,
            "type": "napari_bridge",
            "port": info.get("bridge_port", _external_port),
            "viewer_info": info.get("viewer", {}),
        }
    else:
        viewers["external"] = {"available": False}

    # Check for local viewer
    global _viewer
    if _viewer is not None:
        viewers["local"] = {
            "available": True,
            "type": "singleton",
            "title": _viewer.title,
            "n_layers": len(_viewer.layers),
        }
    else:
        viewers["local"] = {
            "available": True,  # Can be created
            "type": "not_initialized",
        }

    return {
        "status": "ok",
        "viewers": viewers,
    }

init_viewer async staticmethod 

init_viewer(title: str | None = None, width: int | str | None = None, height: int | str | None = None, port: int | str | None = None) -> dict[str, Any]

Create or return the napari viewer (local or external).

Parameters:

Name Type Description Default
title str

Optional window title (only for local viewer).

 None
width int

Optional initial canvas width (only for local viewer).

 None
height int

Optional initial canvas height (only for local viewer).

 None
port int

If provided, attempt to connect to an external napari-mcp bridge on this port (default is taken from NAPARI_MCP_BRIDGE_PORT or 9999).

 None

Returns:

Type Description
dict

Dictionary containing status, viewer type, and layer info.
Source code in src/napari_mcp/server.py 
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@staticmethod
async def init_viewer(
    title: str | None = None,
    width: int | str | None = None,
    height: int | str | None = None,
    port: int | str | None = None,
) -> dict[str, Any]:
    """
    Create or return the napari viewer (local or external).

    Parameters
    ----------
    title : str, optional
        Optional window title (only for local viewer).
    width : int, optional
        Optional initial canvas width (only for local viewer).
    height : int, optional
        Optional initial canvas height (only for local viewer).
    port : int, optional
        If provided, attempt to connect to an external napari-mcp bridge on
        this port (default is taken from NAPARI_MCP_BRIDGE_PORT or 9999).

    Returns
    -------
    dict
        Dictionary containing status, viewer type, and layer info.
    """
    # Allow overriding the external port per-call
    global _external_port
    if port is not None:
        try:
            _external_port = int(port)
        except Exception:
            logger.error("Invalid port: {port}")
            _external_port = _external_port

    async with _viewer_lock:
        # Try external viewer first; fall back to local
        try:
            return await NapariMCPTools._external_session_information(
                _external_port
            )
        except Exception:
            # No external viewer; continue to local viewer
            pass

        # Use local viewer
        v = _ensure_viewer()
        if title:
            v.title = title
        if width or height:
            w = (
                int(width)
                if width is not None
                else v.window.qt_viewer.canvas.size().width()
            )
            h = (
                int(height)
                if height is not None
                else v.window.qt_viewer.canvas.size().height()
            )
            v.window.qt_viewer.canvas.native.resize(w, h)
        # Always ensure GUI pump is running for local viewer (backwards-incompatible change)
        global _qt_pump_task
        app = _ensure_qt_app()
        with contextlib.suppress(Exception):
            app.setQuitOnLastWindowClosed(False)
        _connect_window_destroyed_signal(v)

        # Best-effort to show window without forcing focus (safer for tests/headless)
        try:
            qt_win = v.window._qt_window  # type: ignore[attr-defined]
            qt_win.show()
        except Exception:
            pass

        if _qt_pump_task is None or _qt_pump_task.done():
            loop = asyncio.get_running_loop()
            _qt_pump_task = loop.create_task(_qt_event_pump())

        _process_events()
        return {
            "status": "ok",
            "viewer_type": "local",
            "title": v.title,
            "layers": [lyr.name for lyr in v.layers],
        }

close_viewer async staticmethod 

close_viewer() -> dict[str, Any]

Close the viewer window and clear all layers.

Returns:

Type Description
dict

Dictionary with status: 'closed' if viewer existed, 'no_viewer' if none.
Source code in src/napari_mcp/server.py 
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@staticmethod
async def close_viewer() -> dict[str, Any]:
    """
    Close the viewer window and clear all layers.

    Returns
    -------
    dict
        Dictionary with status: 'closed' if viewer existed, 'no_viewer' if none.
    """
    async with _viewer_lock:
        global _viewer, _qt_pump_task
        if _viewer is not None:
            _viewer.close()
            _viewer = None
            # Stop GUI pump when closing viewer
            if _qt_pump_task is not None and not _qt_pump_task.done():
                _qt_pump_task.cancel()
                with contextlib.suppress(asyncio.CancelledError):
                    await _qt_pump_task
            _qt_pump_task = None
            _process_events()
            return {"status": "closed"}
        return {"status": "no_viewer"}

session_information async staticmethod 

session_information() -> dict[str, Any]

Get comprehensive information about the current napari session.

Returns:

Type Description
dict

Comprehensive session information including viewer state, system info, and environment details.
Source code in src/napari_mcp/server.py 
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@staticmethod
async def session_information() -> dict[str, Any]:
    """
    Get comprehensive information about the current napari session.

    Returns
    -------
    dict
        Comprehensive session information including viewer state, system info,
        and environment details.
    """
    import os
    import platform

    async with _viewer_lock:
        global _viewer, _qt_pump_task, _exec_globals

        try:
            return await NapariMCPTools._external_session_information(
                _external_port
            )
        except Exception:
            # No external viewer; continue to local viewer
            pass

        # Use local viewer

        # Check if viewer exists
        viewer_exists = _viewer is not None
        if not viewer_exists:
            return {
                "status": "ok",
                "session_type": "napari_mcp_standalone_session",
                "timestamp": str(np.datetime64("now")),
                "viewer": None,
                "message": "No viewer currently initialized. Call init_viewer() first.",
            }

        v = _viewer
        assert v is not None  # We already checked this above

        # Viewer information
        viewer_info = {
            "title": v.title,
            "viewer_id": id(v),
            "n_layers": len(v.layers),
            "layer_names": [layer.name for layer in v.layers],
            "selected_layers": [layer.name for layer in v.layers.selection],
            "current_step": dict(enumerate(v.dims.current_step))
            if hasattr(v.dims, "current_step")
            else {},
            "ndisplay": v.dims.ndisplay,
            "camera_center": list(v.camera.center),
            "camera_zoom": float(v.camera.zoom),
            "camera_angles": list(v.camera.angles) if v.camera.angles else [],
            "grid_enabled": v.grid.enabled,
        }

        # System information
        system_info = {
            "python_version": sys.version,
            "platform": platform.platform(),
            "napari_version": getattr(napari, "__version__", "unknown"),
            "process_id": os.getpid(),
            "working_directory": os.getcwd(),
        }

        # Session status
        gui_running = _qt_pump_task is not None and not _qt_pump_task.done()
        session_info = {
            "server_type": "napari_mcp_standalone",
            "viewer_instance": f"<napari.Viewer at {hex(id(v))}>",
            "gui_pump_running": gui_running,
            "execution_namespace_vars": list(_exec_globals.keys()),
            "qt_app_available": _qt_app is not None,
        }

        # Layer details
        layer_details = []
        for layer in v.layers:
            layer_detail = {
                "name": layer.name,
                "type": layer.__class__.__name__,
                "visible": _parse_bool(getattr(layer, "visible", True)),
                "opacity": float(getattr(layer, "opacity", 1.0)),
                "blending": getattr(layer, "blending", None),
                "data_shape": list(layer.data.shape)
                if hasattr(layer, "data") and hasattr(layer.data, "shape")
                else None,
                "data_dtype": str(layer.data.dtype)
                if hasattr(layer, "data") and hasattr(layer.data, "dtype")
                else None,
                "layer_id": id(layer),
            }

            # Add layer-specific properties
            if hasattr(layer, "colormap"):
                layer_detail["colormap"] = getattr(
                    layer.colormap, "name", str(layer.colormap)
                )
            if hasattr(layer, "contrast_limits"):
                try:
                    cl = layer.contrast_limits
                    layer_detail["contrast_limits"] = [float(cl[0]), float(cl[1])]
                except Exception:
                    pass
            if hasattr(layer, "gamma"):
                layer_detail["gamma"] = float(getattr(layer, "gamma", 1.0))

            layer_details.append(layer_detail)

        return {
            "status": "ok",
            "session_type": "napari_mcp_standalone_session",
            "timestamp": str(np.datetime64("now")),
            "viewer": viewer_info,
            "system": system_info,
            "session": session_info,
            "layers": layer_details,
        }

list_layers async staticmethod 

list_layers() -> list[dict[str, Any]]

Return a list of layers with key properties.

Source code in src/napari_mcp/server.py 
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@staticmethod
async def list_layers() -> list[dict[str, Any]]:
    """Return a list of layers with key properties."""
    # Try to proxy to external viewer first
    proxy_result = await _proxy_to_external("list_layers")
    if proxy_result is not None:
        # Ensure the result is the expected list format
        if isinstance(proxy_result, list):
            return proxy_result
        elif isinstance(proxy_result, dict) and "content" in proxy_result:
            content = proxy_result["content"]
            if isinstance(content, list):
                return content
        return []

    # Local execution
    async with _viewer_lock:

        def _build():
            v = _ensure_viewer()
            result: list[dict[str, Any]] = []  # type: ignore
            for lyr in v.layers:
                entry = {
                    "name": lyr.name,
                    "type": lyr.__class__.__name__,
                    "visible": _parse_bool(getattr(lyr, "visible", True)),
                    "opacity": float(getattr(lyr, "opacity", 1.0)),
                    "blending": getattr(lyr, "blending", None),
                }
                if (
                    hasattr(lyr, "colormap")
                    and getattr(lyr, "colormap", None) is not None
                ):
                    entry["colormap"] = getattr(lyr.colormap, "name", None) or str(
                        lyr.colormap
                    )
                if (
                    hasattr(lyr, "contrast_limits")
                    and getattr(lyr, "contrast_limits", None) is not None
                ):
                    try:
                        cl = list(lyr.contrast_limits)
                        entry["contrast_limits"] = [float(cl[0]), float(cl[1])]
                    except Exception:
                        pass
                result.append(entry)
            return result

        return _gui_execute(_build)

add_image async staticmethod 

add_image(path: str, name: str | None = None, colormap: str | None = None, blending: str | None = None, channel_axis: int | str | None = None) -> dict[str, Any]

Add an image layer from a file path.

Parameters:

Name Type Description Default
path str

Path to an image readable by imageio (e.g., PNG, TIFF, OME-TIFF).

 required
name str

Layer name. If None, uses filename.

 None
colormap str

Napari colormap name (e.g., 'gray', 'magma').

 None
blending str

Blending mode (e.g., 'translucent').

 None
channel_axis int

If provided, interpret that axis as channels.

 None

Returns:

Type Description
dict

Dictionary containing status, layer name, and image shape.
Source code in src/napari_mcp/server.py 
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@staticmethod
async def add_image(
    path: str,
    name: str | None = None,
    colormap: str | None = None,
    blending: str | None = None,
    channel_axis: int | str | None = None,
) -> dict[str, Any]:
    """
    Add an image layer from a file path.

    Parameters
    ----------
    path : str
        Path to an image readable by imageio (e.g., PNG, TIFF, OME-TIFF).
    name : str, optional
        Layer name. If None, uses filename.
    colormap : str, optional
        Napari colormap name (e.g., 'gray', 'magma').
    blending : str, optional
        Blending mode (e.g., 'translucent').
    channel_axis : int, optional
        If provided, interpret that axis as channels.

    Returns
    -------
    dict
        Dictionary containing status, layer name, and image shape.
    """
    # Try to proxy to external viewer first
    params: dict[str, Any] = {"path": path}
    if name:
        params["name"] = name
    if colormap:
        params["colormap"] = colormap
    if blending:
        params["blending"] = blending
    if channel_axis is not None:
        params["channel_axis"] = int(channel_axis)

    result = await _proxy_to_external("add_image", params)
    if result is not None:
        return result

    # Local execution
    import imageio.v3 as iio

    async with _viewer_lock:
        data = iio.imread(path)

        def _add():
            v = _ensure_viewer()
            layer = v.add_image(
                data,
                name=name,
                colormap=colormap,
                blending=blending,
                channel_axis=channel_axis,
            )
            _process_events()
            return {
                "status": "ok",
                "name": layer.name,
                "shape": list(np.shape(data)),
            }

        return _gui_execute(_add)

add_labels async staticmethod 

add_labels(path: str, name: str | None = None) -> dict[str, Any]

Add a labels layer from a file path (e.g., PNG/TIFF with integer labels).

Source code in src/napari_mcp/server.py 
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@staticmethod
async def add_labels(path: str, name: str | None = None) -> dict[str, Any]:
    """Add a labels layer from a file path (e.g., PNG/TIFF with integer labels)."""
    import imageio.v3 as iio

    async with _viewer_lock:
        try:
            from pathlib import Path

            def _add():
                v = _ensure_viewer()
                p = Path(path).expanduser().resolve(strict=False)
                data = iio.imread(str(p))
                layer = v.add_labels(data, name=name)
                _process_events()
                return {
                    "status": "ok",
                    "name": layer.name,
                    "shape": list(np.shape(data)),
                }

            return _gui_execute(_add)
        except Exception as e:
            return {
                "status": "error",
                "message": f"Failed to add labels from '{path}': {e}",
            }

add_points async staticmethod 

add_points(points: list[list[float]], name: str | None = None, size: float | str = 10.0) -> dict[str, Any]

Add a points layer.

  • points: List of [y, x] or [z, y, x] coordinates
  • name: Optional layer name
  • size: Point size in pixels
Source code in src/napari_mcp/server.py 
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@staticmethod
async def add_points(
    points: list[list[float]], name: str | None = None, size: float | str = 10.0
) -> dict[str, Any]:
    """
    Add a points layer.

    - points: List of [y, x] or [z, y, x] coordinates
    - name: Optional layer name
    - size: Point size in pixels
    """
    async with _viewer_lock:

        def _add():
            v = _ensure_viewer()
            arr = np.asarray(points, dtype=float)
            layer = v.add_points(arr, name=name, size=float(size))
            _process_events()
            return {
                "status": "ok",
                "name": layer.name,
                "n_points": int(arr.shape[0]),
            }

        return _gui_execute(_add)

remove_layer async staticmethod 

remove_layer(name: str) -> dict[str, Any]

Remove a layer by name.

Source code in src/napari_mcp/server.py 
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@staticmethod
async def remove_layer(name: str) -> dict[str, Any]:
    """Remove a layer by name."""
    async with _viewer_lock:

        def _remove():
            v = _ensure_viewer()
            if name in v.layers:
                v.layers.remove(name)
                _process_events()
                return {"status": "removed", "name": name}
            return {"status": "not_found", "name": name}

        return _gui_execute(_remove)

set_layer_properties async staticmethod 

set_layer_properties(name: str, visible: bool | None = None, opacity: float | None = None, colormap: str | None = None, blending: str | None = None, contrast_limits: list[float] | None = None, gamma: float | str | None = None, new_name: str | None = None) -> dict[str, Any]

Set common properties on a layer by name.

Source code in src/napari_mcp/server.py 
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@staticmethod
async def set_layer_properties(
    name: str,
    visible: bool | None = None,
    opacity: float | None = None,
    colormap: str | None = None,
    blending: str | None = None,
    contrast_limits: list[float] | None = None,
    gamma: float | str | None = None,
    new_name: str | None = None,
) -> dict[str, Any]:
    """Set common properties on a layer by name."""
    async with _viewer_lock:

        def _set():
            v = _ensure_viewer()
            if name not in v.layers:
                return {"status": "not_found", "name": name}
            lyr = v.layers[name]
            if visible is not None and hasattr(lyr, "visible"):
                lyr.visible = _parse_bool(visible)
            if opacity is not None and hasattr(lyr, "opacity"):
                lyr.opacity = float(opacity)
            if colormap is not None and hasattr(lyr, "colormap"):
                lyr.colormap = colormap
            if blending is not None and hasattr(lyr, "blending"):
                lyr.blending = blending
            if contrast_limits is not None and hasattr(lyr, "contrast_limits"):
                with contextlib.suppress(Exception):
                    lyr.contrast_limits = [
                        float(contrast_limits[0]),
                        float(contrast_limits[1]),
                    ]
            if gamma is not None and hasattr(lyr, "gamma"):
                lyr.gamma = float(gamma)
            if new_name is not None:
                lyr.name = new_name
            _process_events()
            return {"status": "ok", "name": lyr.name}

        return _gui_execute(_set)

reorder_layer async staticmethod 

reorder_layer(name: str, index: int | str | None = None, before: str | None = None, after: str | None = None) -> dict[str, Any]

Reorder a layer by name.

Provide exactly one of: - index: absolute target index - before: move before this layer name - after: move after this layer name

Source code in src/napari_mcp/server.py 
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@staticmethod
async def reorder_layer(
    name: str,
    index: int | str | None = None,
    before: str | None = None,
    after: str | None = None,
) -> dict[str, Any]:
    """
    Reorder a layer by name.

    Provide exactly one of:
    - index: absolute target index
    - before: move before this layer name
    - after: move after this layer name
    """
    async with _viewer_lock:

        def _reorder():
            v = _ensure_viewer()
            if name not in v.layers:
                return {"status": "not_found", "name": name}
            if sum(x is not None for x in (index, before, after)) != 1:
                return {
                    "status": "error",
                    "message": "Provide exactly one of index, before, or after",
                }
            cur = v.layers.index(name)
            target = cur
            if index is not None:
                target = max(0, min(int(index), len(v.layers) - 1))
            elif before is not None:
                if before not in v.layers:
                    return {"status": "not_found", "name": before}
                target = v.layers.index(before)
            elif after is not None:
                if after not in v.layers:
                    return {"status": "not_found", "name": after}
                target = v.layers.index(after) + 1
            if target != cur:
                v.layers.move(cur, target)
            _process_events()
            return {"status": "ok", "name": name, "index": v.layers.index(name)}

        return _gui_execute(_reorder)

set_active_layer async staticmethod 

set_active_layer(name: str) -> dict[str, Any]

Set the selected/active layer by name.

Source code in src/napari_mcp/server.py 
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@staticmethod
async def set_active_layer(name: str) -> dict[str, Any]:
    """Set the selected/active layer by name."""
    async with _viewer_lock:

        def _set_active():
            v = _ensure_viewer()
            if name not in v.layers:
                return {"status": "not_found", "name": name}
            v.layers.selection = {v.layers[name]}
            _process_events()
            return {"status": "ok", "active": name}

        return _gui_execute(_set_active)

reset_view async staticmethod 

reset_view() -> dict[str, Any]

Reset the camera view to fit data.

Source code in src/napari_mcp/server.py 
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@staticmethod
async def reset_view() -> dict[str, Any]:
    """Reset the camera view to fit data."""
    async with _viewer_lock:

        def _reset():
            v = _ensure_viewer()
            v.reset_view()
            _process_events()
            return {"status": "ok"}

        return _gui_execute(_reset)

set_camera async staticmethod 

set_camera(center: list[float] | None = None, zoom: float | str | None = None, angle: float | str | None = None) -> dict[str, Any]

Set camera properties: center, zoom, and/or angle.

Source code in src/napari_mcp/server.py 
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@staticmethod
async def set_camera(
    center: list[float] | None = None,
    zoom: float | str | None = None,
    angle: float | str | None = None,
) -> dict[str, Any]:
    """Set camera properties: center, zoom, and/or angle."""
    async with _viewer_lock:

        def _set_cam():
            v = _ensure_viewer()
            if center is not None:
                v.camera.center = list(map(float, center))
            if zoom is not None:
                v.camera.zoom = float(zoom)
            if angle is not None:
                v.camera.angles = (float(angle),)
            _process_events()
            return {
                "status": "ok",
                "center": list(map(float, v.camera.center)),
                "zoom": float(v.camera.zoom),
            }

        return _gui_execute(_set_cam)

set_ndisplay async staticmethod 

set_ndisplay(ndisplay: int | str) -> dict[str, Any]

Set number of displayed dimensions (2 or 3).

Source code in src/napari_mcp/server.py 
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@staticmethod
async def set_ndisplay(ndisplay: int | str) -> dict[str, Any]:
    """Set number of displayed dimensions (2 or 3)."""
    async with _viewer_lock:

        def _set():
            v = _ensure_viewer()
            v.dims.ndisplay = int(ndisplay)
            _process_events()
            return {"status": "ok", "ndisplay": int(v.dims.ndisplay)}

        return _gui_execute(_set)

set_dims_current_step async staticmethod 

set_dims_current_step(axis: int | str, value: int | str) -> dict[str, Any]

Set the current step (slider position) for a specific axis.

Source code in src/napari_mcp/server.py 
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@staticmethod
async def set_dims_current_step(
    axis: int | str, value: int | str
) -> dict[str, Any]:
    """Set the current step (slider position) for a specific axis."""
    async with _viewer_lock:

        def _set():
            v = _ensure_viewer()
            v.dims.set_current_step(int(axis), int(value))
            _process_events()
            return {"status": "ok", "axis": int(axis), "value": int(value)}

        return _gui_execute(_set)

set_grid async staticmethod 

set_grid(enabled: bool | str = True) -> dict[str, Any]

Enable or disable grid view.

Source code in src/napari_mcp/server.py 
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@staticmethod
async def set_grid(enabled: bool | str = True) -> dict[str, Any]:
    """Enable or disable grid view."""
    async with _viewer_lock:

        def _set():
            v = _ensure_viewer()
            v.grid.enabled = _parse_bool(enabled)
            _process_events()
            return {"status": "ok", "grid": _parse_bool(v.grid.enabled)}

        return _gui_execute(_set)

screenshot async staticmethod 

screenshot(canvas_only: bool | str = True) -> ImageContent

Take a screenshot of the napari canvas and return as base64.

Parameters:

Name Type Description Default
canvas_only bool

If True, only capture the canvas area.

 True

Returns:

Type Description
ImageContent

The screenshot image as an mcp.types.ImageContent object.
Source code in src/napari_mcp/server.py 
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@staticmethod
async def screenshot(canvas_only: bool | str = True) -> ImageContent:
    """
    Take a screenshot of the napari canvas and return as base64.

    Parameters
    ----------
    canvas_only : bool, default=True
        If True, only capture the canvas area.

    Returns
    -------
    ImageContent
        The screenshot image as an mcp.types.ImageContent object.
    """
    # Try to proxy to external viewer first
    result = await _proxy_to_external("screenshot", {"canvas_only": canvas_only})
    if result is not None:
        return result

    # Local execution
    async with _viewer_lock:

        def _shot():
            v = _ensure_viewer()
            _process_events(3)
            arr = v.screenshot(canvas_only=canvas_only)
            if not isinstance(arr, np.ndarray):
                arr = np.asarray(arr)
            if arr.dtype != np.uint8:
                arr = arr.astype(np.uint8, copy=False)
            img = Image.fromarray(arr)
            buf = BytesIO()
            img.save(buf, format="PNG")
            enc = buf.getvalue()
            return fastmcp.utilities.types.Image(
                data=enc, format="png"
            ).to_image_content()

        return _gui_execute(_shot)

timelapse_screenshot async staticmethod 

timelapse_screenshot(axis: int | str, slice_range: str, canvas_only: bool | str = True, interpolate_to_fit: bool = True) -> list[ImageContent]

Capture a series of screenshots while sweeping a dims axis.

Parameters:

Name Type Description Default
axis int

Dims axis index to sweep (e.g., temporal axis).

 required
slice_range str

Python-like slice string over step indices, e.g. "1:5", ":6", "::2". Defaults follow Python semantics with start=0, stop=nsteps, step=1.

 required
canvas_only bool

If True, only capture the canvas area.

 True
interpolate_to_fit bool

If True, interpolate the images to fit the total size cap of 1309246 bytes.

 False

Returns:

Type Description
list[ImageContent]

List of screenshots as mcp.types.ImageContent objects.
Source code in src/napari_mcp/server.py 
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@staticmethod
async def timelapse_screenshot(
    axis: int | str,
    slice_range: str,
    canvas_only: bool | str = True,
    interpolate_to_fit: bool = True,
) -> list[ImageContent]:
    """
    Capture a series of screenshots while sweeping a dims axis.

    Parameters
    ----------
    axis : int
        Dims axis index to sweep (e.g., temporal axis).
    slice_range : str
        Python-like slice string over step indices, e.g. "1:5", ":6", "::2".
        Defaults follow Python semantics with start=0, stop=nsteps, step=1.
    canvas_only : bool, default=True
        If True, only capture the canvas area.
    interpolate_to_fit : bool, default=False
        If True, interpolate the images to fit the total size cap of 1309246 bytes.

    Returns
    -------
    list[ImageContent]
        List of screenshots as mcp.types.ImageContent objects.
    """
    max_total_base64_bytes = 1309246 if interpolate_to_fit else None

    # Try to proxy to external viewer first
    result = await _proxy_to_external(
        "timelapse_screenshot",
        {
            "axis": axis,
            "slice_range": slice_range,
            "canvas_only": canvas_only,
            "interpolate_to_fit": interpolate_to_fit,
        },
    )
    if result is not None:
        return result  # type: ignore[return-value]

    def _parse_slice(spec: str, length: int) -> list[int]:
        # Normalize
        s = (spec or "").strip()
        # Single integer
        if s and ":" not in s:
            try:
                idx = int(s)
            except Exception as err:
                raise ValueError(f"Invalid slice range: {spec!r}") from err
            if idx < 0:
                idx += length
            if not (0 <= idx < length):
                raise ValueError(
                    f"Index out of bounds for axis with {length} steps: {idx}"
                )
            return [idx]

        # Slice form start:stop:step
        parts = s.split(":")
        if len(parts) > 3:
            raise ValueError(f"Invalid slice range: {spec!r}")
        start_s, stop_s, step_s = (parts + [""] * 3)[:3]

        def _to_int_or_none(val: str) -> int | None:
            v = val.strip()
            if v == "":
                return None
            return int(v)

        start = _to_int_or_none(start_s)
        stop = _to_int_or_none(stop_s)
        step = _to_int_or_none(step_s) or 1
        if step == 0:
            raise ValueError("slice step cannot be 0")

        # Handle negatives like Python
        if start is None:
            start = 0 if step > 0 else length - 1
        if stop is None:
            stop = length if step > 0 else -1
        if start < 0:
            start += length
        if stop < 0:
            stop += length

        # Clamp to valid iteration range similar to range() behavior
        rng = range(start, stop, step)
        indices = [i for i in rng if 0 <= i < length]
        return indices

    # Local execution
    async with _viewer_lock:

        def _run_series():
            v = _ensure_viewer()

            # Determine number of steps along axis
            try:
                nsteps_tuple = getattr(v.dims, "nsteps", None)
                if nsteps_tuple is None:
                    # Fallback: infer from current_step length and a conservative stop
                    # We cannot reliably infer total steps without dims.nsteps; require it
                    raise AttributeError
                total = int(nsteps_tuple[int(axis)])
            except Exception:
                # Best effort via bounds from layers; may be approximate
                try:
                    total = max(
                        int(getattr(lyr.data, "shape", [1])[(int(axis))])
                        if int(axis) < getattr(lyr.data, "ndim", 0)
                        else 1
                        for lyr in v.layers
                    )
                except Exception:
                    total = 0

            if total <= 0:
                raise RuntimeError(
                    "Unable to determine number of steps for the given axis"
                )

            indices = _parse_slice(slice_range, total)
            if not indices:
                return []

            # Take a sample at the first index to estimate size
            v.dims.set_current_step(int(axis), int(indices[0]))
            _process_events(2)
            sample_arr = v.screenshot(canvas_only=canvas_only)
            if not isinstance(sample_arr, np.ndarray):
                sample_arr = np.asarray(sample_arr)
            if sample_arr.dtype != np.uint8:
                sample_arr = sample_arr.astype(np.uint8, copy=False)
            sample_img = Image.fromarray(sample_arr)
            sbuf = BytesIO()
            sample_img.save(sbuf, format="PNG")
            sample_png = sbuf.getvalue()
            sample_b64_len = ((len(sample_png) + 2) // 3) * 4

            # Ask user whether to downsample if estimated total exceeds cap
            downsample_factor = 1.0
            if (
                max_total_base64_bytes is not None
                and sample_b64_len * len(indices) > max_total_base64_bytes
            ):
                est_factor = math.sqrt(
                    max_total_base64_bytes
                    / float(max(1, sample_b64_len * len(indices)))
                )
                downsample_factor = max(0.05, min(1.0, est_factor))

            images: list[ImageContent] = []  # type: ignore
            total_b64_len = 0
            for idx in indices:
                # Move slider
                v.dims.set_current_step(int(axis), int(idx))
                _process_events(2)

                # Capture
                arr = v.screenshot(canvas_only=canvas_only)
                if not isinstance(arr, np.ndarray):
                    arr = np.asarray(arr)
                if arr.dtype != np.uint8:
                    arr = arr.astype(np.uint8, copy=False)

                img = Image.fromarray(arr)
                if downsample_factor < 1.0:
                    new_w = max(1, int(img.width * downsample_factor))
                    new_h = max(1, int(img.height * downsample_factor))
                    if new_w != img.width or new_h != img.height:
                        img = img.resize((new_w, new_h), resample=Image.BILINEAR)
                buf = BytesIO()
                img.save(buf, format="PNG")
                enc = buf.getvalue()
                b64_len = ((len(enc) + 2) // 3) * 4
                if (
                    max_total_base64_bytes is not None
                    and total_b64_len + b64_len > max_total_base64_bytes
                ):
                    break
                total_b64_len += b64_len
                images.append(
                    fastmcp.utilities.types.Image(
                        data=enc, format="png"
                    ).to_image_content()
                )

            return images

        return _gui_execute(_run_series)

execute_code async staticmethod 

execute_code(code: str, line_limit: int | str = 30) -> dict[str, Any]

Execute arbitrary Python code in the server's interpreter.

Similar to napari's console. The execution namespace persists across calls and includes 'viewer', 'napari', and 'np'.

Parameters:

Name Type Description Default
code str

Python code string. The value of the last expression (if any) is returned as 'result_repr'.

 required
line_limit int

Maximum number of output lines to return. Use -1 for unlimited output. Warning: Using -1 may consume a large number of tokens.

 30

Returns:

Type Description
dict

Dictionary with 'status', optional 'result_repr', 'stdout', 'stderr', and 'output_id' for retrieving full output if truncated.
Source code in src/napari_mcp/server.py 
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@staticmethod
async def execute_code(code: str, line_limit: int | str = 30) -> dict[str, Any]:
    """
    Execute arbitrary Python code in the server's interpreter.

    Similar to napari's console. The execution namespace persists across calls
    and includes 'viewer', 'napari', and 'np'.

    Parameters
    ----------
    code : str
        Python code string. The value of the last expression (if any)
        is returned as 'result_repr'.
    line_limit : int, default=30
        Maximum number of output lines to return. Use -1 for unlimited output.
        Warning: Using -1 may consume a large number of tokens.

    Returns
    -------
    dict
        Dictionary with 'status', optional 'result_repr', 'stdout', 'stderr',
        and 'output_id' for retrieving full output if truncated.
    """
    # Try to proxy to external viewer first
    result = await _proxy_to_external("execute_code", {"code": code})
    if result is not None:
        return result

    # Local execution
    async with _viewer_lock:
        v = _ensure_viewer()
        _exec_globals.setdefault("__builtins__", __builtins__)  # type: ignore[assignment]
        _exec_globals["viewer"] = v
        napari_mod = napari
        if napari_mod is not None:
            _exec_globals.setdefault("napari", napari_mod)
        _exec_globals.setdefault("np", np)

        stdout_buf = StringIO()
        stderr_buf = StringIO()
        result_repr: str | None = None
        try:
            # Capture stdout/stderr during execution
            with (
                contextlib.redirect_stdout(stdout_buf),
                contextlib.redirect_stderr(stderr_buf),
            ):
                # Try to evaluate last expression if present
                parsed = ast.parse(code, mode="exec")
                if parsed.body and isinstance(parsed.body[-1], ast.Expr):
                    # Execute all but last, then eval last expression to
                    # capture a result
                    if len(parsed.body) > 1:
                        exec_ast = ast.Module(
                            body=parsed.body[:-1], type_ignores=[]
                        )
                        exec(
                            compile(exec_ast, "<mcp-exec>", "exec"),
                            _exec_globals,
                            _exec_globals,
                        )
                    last_expr = ast.Expression(body=parsed.body[-1].value)
                    value = eval(
                        compile(last_expr, "<mcp-eval>", "eval"),
                        _exec_globals,
                        _exec_globals,
                    )
                    result_repr = repr(value)
                else:
                    # Pure statements
                    exec(
                        compile(parsed, "<mcp-exec>", "exec"),
                        _exec_globals,
                        _exec_globals,
                    )
            _process_events(2)

            # Get full output
            stdout_full = stdout_buf.getvalue()
            stderr_full = stderr_buf.getvalue()

            # Store full output and get ID
            output_id = await _store_output(
                tool_name="execute_code",
                stdout=stdout_full,
                stderr=stderr_full,
                result_repr=result_repr,
                code=code,
            )

            # Prepare response with line limiting
            response = {
                "status": "ok",
                "output_id": output_id,
                **({"result_repr": result_repr} if result_repr is not None else {}),
            }

            # Add warning for unlimited output
            if line_limit == -1:
                response["warning"] = (
                    "Unlimited output requested. This may consume a large number "
                    "of tokens. Consider using read_output for large outputs."
                )
                response["stdout"] = stdout_full
                response["stderr"] = stderr_full
            else:
                # Truncate stdout and stderr
                stdout_truncated, stdout_was_truncated = _truncate_output(
                    stdout_full, int(line_limit)
                )
                stderr_truncated, stderr_was_truncated = _truncate_output(
                    stderr_full, int(line_limit)
                )

                response["stdout"] = stdout_truncated
                response["stderr"] = stderr_truncated

                if stdout_was_truncated or stderr_was_truncated:
                    response["truncated"] = True  # type: ignore
                    response["message"] = (
                        f"Output truncated to {line_limit} lines. "
                        f"Use read_output('{output_id}') to retrieve full output."
                    )

            return response
        except Exception as e:
            _process_events(1)
            tb = traceback.format_exc()

            # Get full output including traceback
            stdout_full = stdout_buf.getvalue()
            stderr_full = stderr_buf.getvalue() + tb

            # Store full output and get ID
            output_id = await _store_output(
                tool_name="execute_code",
                stdout=stdout_full,
                stderr=stderr_full,
                code=code,
                error=True,
            )

            # Prepare error response with line limiting
            response = {
                "status": "error",
                "output_id": output_id,
            }

            # Add warning for unlimited output
            if line_limit == -1:
                response["warning"] = (
                    "Unlimited output requested. This may consume a large number "
                    "of tokens. Consider using read_output for large outputs."
                )
                response["stdout"] = stdout_full
                response["stderr"] = stderr_full
            else:
                # Truncate stdout and stderr
                stdout_truncated, stdout_was_truncated = _truncate_output(
                    stdout_full, int(line_limit)
                )
                stderr_truncated, stderr_was_truncated = _truncate_output(
                    stderr_full, int(line_limit)
                )

                response["stdout"] = stdout_truncated
                # Ensure exception summary is present even when truncated
                error_summary = f"{type(e).__name__}: {e}"
                if error_summary not in stderr_truncated:
                    # Append a concise summary line so callers can see the error type
                    if stderr_truncated and not stderr_truncated.endswith("\n"):
                        stderr_truncated += "\n"
                    stderr_truncated += error_summary + "\n"
                response["stderr"] = stderr_truncated

                if stdout_was_truncated or stderr_was_truncated:
                    response["truncated"] = True  # type: ignore
                    response["message"] = (
                        f"Output truncated to {line_limit} lines. "
                        f"Use read_output('{output_id}') to retrieve full output."
                    )

            return response

install_packages async staticmethod 

install_packages(packages: list[str], upgrade: bool | None = False, no_deps: bool | None = False, index_url: str | None = None, extra_index_url: str | None = None, pre: bool | None = False, line_limit: int | str = 30, timeout: int = 240) -> dict[str, Any]

Install Python packages using pip.

Install packages into the currently running server environment.

Parameters:

Name Type Description Default
packages list of str

List of package specifiers (e.g., "scikit-image", "torch==2.3.1").

 required
upgrade bool

If True, pass --upgrade flag.

 False
no_deps bool

If True, pass --no-deps flag.

 False
index_url str

Custom index URL.

 None
extra_index_url str

Extra index URL.

 None
pre bool

Allow pre-releases (--pre flag).

 False
line_limit int

Maximum number of output lines to return. Use -1 for unlimited output. Warning: Using -1 may consume a large number of tokens.

 30
timeout int

Timeout for pip install in seconds.

 240

Returns:

Type Description
dict

Dictionary including status, returncode, stdout, stderr, command, and output_id for retrieving full output if truncated.
Source code in src/napari_mcp/server.py 
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@staticmethod
async def install_packages(
    packages: list[str],
    upgrade: bool | None = False,
    no_deps: bool | None = False,
    index_url: str | None = None,
    extra_index_url: str | None = None,
    pre: bool | None = False,
    line_limit: int | str = 30,
    timeout: int = 240,
) -> dict[str, Any]:
    """
    Install Python packages using pip.

    Install packages into the currently running server environment.

    Parameters
    ----------
    packages : list of str
        List of package specifiers (e.g., "scikit-image", "torch==2.3.1").
    upgrade : bool, optional
        If True, pass --upgrade flag.
    no_deps : bool, optional
        If True, pass --no-deps flag.
    index_url : str, optional
        Custom index URL.
    extra_index_url : str, optional
        Extra index URL.
    pre : bool, optional
        Allow pre-releases (--pre flag).
    line_limit : int, default=30
        Maximum number of output lines to return. Use -1 for unlimited output.
        Warning: Using -1 may consume a large number of tokens.
    timeout : int, default=240
        Timeout for pip install in seconds.

    Returns
    -------
    dict
        Dictionary including status, returncode, stdout, stderr, command,
        and output_id for retrieving full output if truncated.
    """
    # Try to proxy to external viewer first
    result = await _proxy_to_external(
        "install_packages",
        {
            "packages": packages,
            "upgrade": upgrade,
            "no_deps": no_deps,
            "index_url": index_url,
            "extra_index_url": extra_index_url,
            "pre": pre,
            "line_limit": line_limit,
            "timeout": timeout,
        },
    )
    if result is not None:
        return result

    if not packages or not isinstance(packages, list):
        return {
            "status": "error",
            "message": "Parameter 'packages' must be a non-empty list of package names",
        }

    cmd: list[str] = [
        sys.executable,
        "-m",
        "pip",
        "install",
        "--no-input",
        "--disable-pip-version-check",
    ]
    if upgrade:
        cmd.append("--upgrade")
    if no_deps:
        cmd.append("--no-deps")
    if pre:
        cmd.append("--pre")
    if index_url:
        cmd.extend(["--index-url", index_url])
    if extra_index_url:
        cmd.extend(["--extra-index-url", extra_index_url])
    cmd.extend(packages)

    # Run pip as a subprocess without blocking the event loop
    proc = await asyncio.create_subprocess_exec(
        *cmd,
        stdout=asyncio.subprocess.PIPE,
        stderr=asyncio.subprocess.PIPE,
    )
    try:
        stdout_b, stderr_b = await asyncio.wait_for(
            proc.communicate(), timeout=timeout
        )
    except asyncio.TimeoutError:
        with contextlib.suppress(ProcessLookupError):
            proc.kill()
        stdout_b, stderr_b = b"", f"pip install timed out after {timeout}s".encode()
    stdout = stdout_b.decode(errors="replace")
    stderr = stderr_b.decode(errors="replace")

    status = "ok" if proc.returncode == 0 else "error"
    command_str = " ".join(shlex.quote(part) for part in cmd)

    # Store full output and get ID
    output_id = await _store_output(
        tool_name="install_packages",
        stdout=stdout,
        stderr=stderr,
        packages=packages,
        command=command_str,
        returncode=proc.returncode,
    )

    # Prepare response with line limiting
    response = {
        "status": status,
        "returncode": proc.returncode if proc.returncode is not None else -1,
        "command": command_str,
        "output_id": output_id,
    }

    # Add warning for unlimited output
    if line_limit == -1:
        response["warning"] = (
            "Unlimited output requested. This may consume a large number "
            "of tokens. Consider using read_output for large outputs."
        )
        response["stdout"] = stdout
        response["stderr"] = stderr
    else:
        # Truncate stdout and stderr
        stdout_truncated, stdout_was_truncated = _truncate_output(
            stdout, int(line_limit)
        )
        stderr_truncated, stderr_was_truncated = _truncate_output(
            stderr, int(line_limit)
        )

        response["stdout"] = stdout_truncated
        response["stderr"] = stderr_truncated

        if stdout_was_truncated or stderr_was_truncated:
            response["truncated"] = True
            response["message"] = (
                f"Output truncated to {line_limit} lines. "
                f"Use read_output('{output_id}') to retrieve full output."
            )

    return response

read_output async staticmethod 

read_output(output_id: str, start: int | str = 0, end: int | str = -1) -> dict[str, Any]

Read stored tool output with optional line range.

Parameters:

Name Type Description Default
output_id str

Unique ID of the stored output.

 required
start int

Starting line number (0-indexed).

 0
end int

Ending line number (exclusive). If -1, read to end.

 -1

Returns:

Type Description
dict

Dictionary containing the requested output lines and metadata.
Source code in src/napari_mcp/server.py 
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@staticmethod
async def read_output(
    output_id: str, start: int | str = 0, end: int | str = -1
) -> dict[str, Any]:
    """
    Read stored tool output with optional line range.

    Parameters
    ----------
    output_id : str
        Unique ID of the stored output.
    start : int, default=0
        Starting line number (0-indexed).
    end : int, default=-1
        Ending line number (exclusive). If -1, read to end.

    Returns
    -------
    dict
        Dictionary containing the requested output lines and metadata.
    """
    async with _output_storage_lock:
        if output_id not in _output_storage:
            return {
                "status": "error",
                "message": f"Output ID '{output_id}' not found",
            }

        stored_output = _output_storage[output_id]

        # Combine stdout and stderr for line-based access
        full_output = ""
        if stored_output.get("stdout"):
            full_output = stored_output["stdout"]
        if stored_output.get("stderr"):
            stderr_text = stored_output["stderr"]
            if (
                full_output
                and not full_output.endswith("\n")
                and not stderr_text.startswith("\n")
            ):
                full_output += "\n"
            full_output += stderr_text

        # Normalize and clamp range inputs
        try:
            start = int(start)
        except Exception:
            start = 0
        try:
            end = int(end)
        except Exception:
            end = -1

        start = max(0, start)

        lines = full_output.splitlines(keepends=True)
        total_lines = len(lines)

        # Handle line range
        end = total_lines if end == -1 else min(total_lines, end)

        selected_lines = [] if start >= total_lines else lines[start:end]

        return {
            "status": "ok",
            "output_id": output_id,
            "tool_name": stored_output["tool_name"],
            "timestamp": stored_output["timestamp"],
            "lines": selected_lines,
            "line_range": {"start": start, "end": min(end, total_lines)},
            "total_lines": total_lines,
            "result_repr": stored_output.get("result_repr"),
        }