python-mcp-server-generator

by github

Complete Python MCP server project generator with tools, resources, and proper configuration. Scaffolds a new Python project using uv with MCP SDK, proper directory structure, and .gitignore Supports both stdio (local) and streamable-http (remote) transport types with optional host, port, and stateless mode configuration Generates decorated tools, resources, and prompts with automatic schema generation from type hints and docstrings Includes comprehensive error handling, async/await support,... npx skills add https://github.com/github/awesome-copilot --skill python-mcp-server-generator Download ZIPGitHub36.2k

Generate Python MCP Server

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

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

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.