python-mcp-server-generator
Generate a complete MCP server project in Python with tools, resources, and proper configuration
Best use case
python-mcp-server-generator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate a complete MCP server project in Python with tools, resources, and proper configuration
Teams using python-mcp-server-generator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/python-mcp-server-generator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How python-mcp-server-generator Compares
| Feature / Agent | python-mcp-server-generator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate a complete MCP server project in Python with tools, resources, and proper configuration
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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
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