gabeujin/python-mcp-server-generator

Generate Python MCP Server

Create a complete Model Context Protocol (MCP) server in Python with the following specifications:

Requirements

1. Project Structure: Create a new Python project with proper structure using uv
2. Dependencies: Include mcp[cli] package with uv
3. Transport Type: Choose between stdio (for local) or streamable-http (for remote)
4. Tools: Create at least one useful tool with proper type hints
5. Error Handling: Include comprehensive error handling and validation

Implementation Details

Project Setup

• Initialize with uv init project-name
• Add MCP SDK: uv add "mcp[cli]"
• Create main server file (e.g., server.py)
• Add .gitignore for Python projects
• Configure for direct execution with if __name__ == "__main__"

Server Configuration

• Use FastMCP class from mcp.server.fastmcp
• Set server name and optional instructions
• Choose transport: stdio (default) or streamable-http
• For HTTP: optionally configure host, port, and stateless mode

Tool Implementation

• Use @mcp.tool() decorator on functions
• Always include type hints - they generate schemas automatically
• Write clear docstrings - they become tool descriptions
• Use Pydantic models or TypedDicts for structured outputs
• Support async operations for I/O-bound tasks
• Include proper error handling

Resource/Prompt Setup (Optional)

• Add resources with @mcp.resource() decorator
• Use URI templates for dynamic resources: "resource://{param}"
• Add prompts with @mcp.prompt() decorator
• Return strings or Message lists from prompts

Code Quality

• Use type hints for all function parameters and returns
• Write docstrings for tools, resources, and prompts
• Follow PEP 8 style guidelines
• Use async/await for asynchronous operations
• Implement context managers for resource cleanup
• Add inline comments for complex logic

Example Tool Types to Consider

• Data processing and transformation
• File system operations (read, analyze, search)
• External API integrations
• Database queries
• Text analysis or generation (with sampling)
• System information retrieval
• Math or scientific calculations

Configuration Options

• For stdio Servers:

  • Simple direct execution
  • Test with uv run mcp dev server.py
  • Install to Claude: uv run mcp install server.py

• For HTTP Servers:

  • Port configuration via environment variables
  • Stateless mode for scalability: stateless_http=True
  • JSON response mode: json_response=True
  • CORS configuration for browser clients
  • Mounting to existing ASGI servers (Starlette/FastAPI)

Testing Guidance

• Explain how to run the server:
  • stdio: python server.py or uv run server.py
  • HTTP: python server.py then connect to http://localhost:PORT/mcp
• Test with MCP Inspector: uv run mcp dev server.py
• Install to Claude Desktop: uv run mcp install server.py
• Include example tool invocations
• Add troubleshooting tips

Additional Features to Consider

• Context usage for logging, progress, and notifications
• LLM sampling for AI-powered tools
• User input elicitation for interactive workflows
• Lifespan management for shared resources (databases, connections)
• Structured output with Pydantic models
• Icons for UI display
• Image handling with Image class
• Completion support for better UX

Best Practices

• Use type hints everywhere - they're not optional
• Return structured data when possible
• Log to stderr (or use Context logging) to avoid stdout pollution
• Clean up resources properly
• Validate inputs early
• Provide clear error messages
• Test tools independently before LLM integration

Generate a complete, production-ready MCP server with type safety, proper error handling, and comprehensive documentation.