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sammorrowdrums/mcp-python-starter

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A starter MCP server in Python.

概要

A feature-complete Model Context Protocol (MCP) server template in Python using FastMCP. This starter demonstrates all major MCP features with clean, Pythonic code. - Python 3.11+ - uv (recommended) or pip This project includes VS Code configuration for seamless development: 1. Open the project in VS Code 2. The MCP configuration is in .vscode/mcp.json 3. Test the server using VS Code's MCP tools 1. Install the Dev Containers extension 2. Open command palette: "Dev Containers: Reopen in Container" 3. Everything is pre-configured and ready to use! Python scripts reload automatically when run with uv run. For enhanced debugging, use mcp dev which provides the MCP Inspector UI. The MCP Inspector is an essential development tool for testing and debugging MCP servers.

README

MCP Python Starter

A feature-complete Model Context Protocol (MCP) server template in Python using FastMCP. This starter demonstrates all major MCP features with clean, Pythonic code.

📚 Documentation

✨ Features

Category Feature Description
Tools hello Basic tool with annotations
get_weather Tool returning structured data
ask_llm Tool that invokes LLM sampling
long_task Tool with 5-second progress updates
load_bonus_tool Dynamically loads a new tool
Resources info://about Static informational resource
file://example.md File-based markdown resource
Templates greeting://{name} Personalized greeting
data://items/{id} Data lookup by ID
Prompts greet Greeting in various styles
code_review Code review with focus areas

🚀 Quick Start

Prerequisites

Installation

# Clone the repository
git clone https://github.com/SamMorrowDrums/mcp-python-starter.git
cd mcp-python-starter

# Install with uv (recommended)
uv sync

# Or with pip
pip install -e .

Running the Server

stdio transport (for local development):

uv run mcp-python-starter --stdio

HTTP transport (for remote/web deployment):

uv run mcp-python-starter --http --port 3000

🔧 VS Code Integration

This project includes VS Code configuration for seamless development:

  1. Open the project in VS Code
  2. The MCP configuration is in .vscode/mcp.json
  3. Test the server using VS Code’s MCP tools

Using DevContainers

  1. Install the Dev Containers extension
  2. Open command palette: “Dev Containers: Reopen in Container”
  3. Everything is pre-configured and ready to use!

📁 Project Structure

.
├── mcp_starter/
│   ├── __init__.py
│   ├── tools.py       # Tool definitions (hello, get_weather, ask_llm, etc.)
│   ├── resources.py   # Resource and template definitions
│   ├── prompts.py     # Prompt definitions
│   └── server.py      # Server orchestration (imports and wires modules)
├── .vscode/
│   ├── mcp.json       # MCP server configuration
│   ├── settings.json  # Python settings
│   └── extensions.json
├── .devcontainer/
│   └── devcontainer.json
├── pyproject.toml     # Project configuration (uv/pip, Ruff config)
└── .python-version

🛠️ Development

# Run the server (Python reloads automatically on changes)
uv run mcp-python-starter --stdio

# Use MCP Inspector for debugging
uv run mcp dev mcp_starter/server.py

# Format code
uv run ruff format .

# Lint
uv run ruff check .

# Lint with auto-fix
uv run ruff check --fix .

# Type check
uv run pyright

Live Reload

Python scripts reload automatically when run with uv run. For enhanced debugging, use mcp dev which provides the MCP Inspector UI.

🔍 MCP Inspector

The MCP Inspector is an essential development tool for testing and debugging MCP servers.

Running Inspector

npx @modelcontextprotocol/inspector -- uv run mcp-python-starter

What Inspector Provides

  • Tools Tab: List and invoke all registered tools with parameters
  • Resources Tab: Browse and read resources and templates
  • Prompts Tab: View and test prompt templates
  • Logs Tab: See JSON-RPC messages between client and server
  • Schema Validation: Verify tool input/output schemas

Debugging Tips

  1. Start Inspector before connecting your IDE/client
  2. Use the “Logs” tab to see exact request/response payloads
  3. Test tool annotations (ToolAnnotations) are exposed correctly
  4. Verify progress notifications appear for long_task
  5. Check that Context injection works for sampling tools

📖 Feature Examples

Tool with Annotations (FastMCP decorator)

@mcp.tool(
    title="Say Hello",
    description="A friendly greeting tool",
    annotations={"readOnlyHint": True},
)
def hello(name: str) -> str:
    """Say hello to someone.
    
    Args:
        name: The name to greet
    """
    return f"Hello, {name}!"

Resource Template

@mcp.resource("greeting://{name}")
def greeting_template(name: str) -> str:
    """Generate a personalized greeting."""
    return f"Hello, {name}!"

Tool with Progress Updates

@mcp.tool(title="Long Task")
async def long_task(
    task_name: str,
    ctx: Context[ServerSession, None],
) -> str:
    for i in range(5):
        await ctx.report_progress(
            progress=i / 5,
            total=1.0,
            message=f"Step {i + 1}/5",
        )
        await asyncio.sleep(1.0)
    return "Done!"

Tool with Sampling

@mcp.tool(title="Ask LLM")
async def ask_llm(
    prompt: str,
    ctx: Context[ServerSession, None],
) -> str:
    result = await ctx.session.create_message(
        messages=[{"role": "user", "content": {"type": "text", "text": prompt}}],
        max_tokens=100,
    )
    return result.content.text

🔐 Environment Variables

Copy .env.example to .env and configure:

cp .env.example .env

🤝 Contributing

Contributions welcome! Please ensure your changes maintain feature parity with other language starters.

📄 License

MIT License - see LICENSE for details.

View this README on GitHub

インストール

npx @modelcontextprotocol/inspector -- uv run mcp-python-starter