python-mcp-server-generator

SKILL.md

Generate Python MCP Server

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

Requirements

Project Structure: Create a new Python project with proper structure using uv

Dependencies: Include mcp[cli] package with uv

Transport Type: Choose between stdio (for local) or streamable-http (for remote)

Tools: Create at least one useful tool with proper type hints

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.