PaperclipOrg
Claude Agent Skill · by Github

Python Mcp Server Generator

The python-mcp-server-generator skill generates a complete Model Context Protocol (MCP) server project in Python, including proper project structure, tools with

Install
Terminal · npx
$npx skills add https://github.com/github/awesome-copilot --skill python-mcp-server-generator
Works with Paperclip

How Python Mcp Server Generator fits into a Paperclip company.

Python Mcp Server Generator drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

S
SaaS FactoryPaired

Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

$27$59
Explore pack
Source file
SKILL.md105 lines
Expand
---name: python-mcp-server-generatordescription: 'Generate a complete MCP server project in Python with tools, resources, and proper configuration'--- # 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 uv2. **Dependencies**: Include mcp[cli] package with uv3. **Transport Type**: Choose between stdio (for local) or streamable-http (for remote)4. **Tools**: Create at least one useful tool with proper type hints5. **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.