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.