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Python Integration Examples

Working examples demonstrating how to use napari MCP server in custom Python scripts for workflow automation.

📁 Available Examples

1. OpenAI Integration (openai_integration.py)

Description: Use OpenAI GPT-4 with napari MCP server for AI-controlled image analysis.

Use case: Automated workflows where GPT-4 decides which napari operations to perform.

Run: 

# Set your API key
export OPENAI_API_KEY="your-key-here"

# Run with installed packages
python openai_integration.py

# Or with uv (zero-install)
uv run --with openai --with mcp python openai_integration.py

What it does: - Connects OpenAI GPT-4 to napari MCP server - Lists available napari tools - Uses GPT-4 to generate code for creating test images - Executes the code in napari environment

2. Anthropic Claude Integration (anthropic_integration.py)

Description: Use Anthropic Claude with napari MCP server for intelligent microscopy analysis.

Use case: Automated workflows with Claude's advanced reasoning capabilities.

Run: 

# Set your API key
export ANTHROPIC_API_KEY="your-key-here"

# Run with installed packages
python anthropic_integration.py

# Or with uv (zero-install)
uv run --with anthropic --with mcp python anthropic_integration.py

What it does: - Connects Claude 3.5 Sonnet to napari MCP server - Converts napari tools to Claude format - Asks Claude to take a screenshot - Executes napari tools based on Claude's decisions

3. Direct MCP Client (direct_mcp_client.py)

Description: Direct napari MCP automation without external LLMs.

Use case: Scripted workflows, batch processing, automated testing.

Run: 

# No API key needed!
python direct_mcp_client.py

# Or with uv
uv run --with mcp python direct_mcp_client.py

What it does: - Creates synthetic test data in napari - Lists all layers - Takes a screenshot - Gets session information - All without any external AI - pure automation

🎯 Use Cases

Automated Image Processing Pipelines

Use these examples to build: - Batch processing - Process hundreds of images automatically - Quality control - Automated checks with AI assistance - Data augmentation - Generate training data with napari - Reporting - Automated analysis reports with screenshots

Research Workflows

Apply to: - Reproducible analysis - Script entire analysis pipelines - Multi-modal AI - Combine vision models with napari control - Interactive notebooks - Jupyter integration with AI assistance - Custom tools - Build specialized analysis applications

Integration Projects

Embed into: - Web applications - Flask/FastAPI backends with napari - Desktop applications - Qt apps with napari + AI - Cloud pipelines - Serverless image processing - CI/CD workflows - Automated testing with napari

🚀 Getting Started

  1. Choose your example based on your use case
  2. Install dependencies: 
    pip install napari-mcp openai anthropic mcp
  3. Set API keys (for OpenAI/Anthropic examples)
  4. Run the script and modify for your needs

📚 Documentation

These examples are starting points - customize them for your specific workflows! 🔬✨