mcp-server-template-python

# mcp-server-template-python A very simple Python template for building MCP servers using Streamable HTTP transport. ## Overview This template provides a foundation for creating MCP servers that can communicate with AI assistants and other MCP clients. It includes a simple HTTP server implementation with example tools, resources & prompts to help you get started building your own MCP integrations. ## Deploy Use the following button to clone the repository and directly deploy the server to Alpic [](https://app.alpic.ai/new/clone?repositoryUrl=https%3A%2F%2Fgithub.com%2Falpic-ai%2Fmcp-server-template-python) ## Prerequisites - Install uv (https://docs.astral.sh/uv/getting-started/installation/) ## Installation 1. Clone the repository: ```bash git clone git@github.com:alpic-ai/mcp-server-template-python.git cd mcp-server-template-python ``` 2. Install python version & dependencies: ```bash uv python install uv sync --locked ``` ## Usage Start the server on port 3000: ```bash uv run main.py ``` ## Running the Inspector ### Requirements - Node.js: ^22.7.5 ### Quick Start (UI mode) To get up and running right away with the UI, just execute the following: ```bash npx @modelcontextprotocol/inspector ``` The inspector server will start up and the UI will be accessible at http://localhost:6274. You can test your server locally by selecting: - Transport Type: Streamable HTTP - URL: http://127.0.0.1:3000/mcp ## Development ### Adding New Tools To add a new tool, modify `main.py`: ```python @mcp.tool( title="Your Tool Name", description="Tool Description for the LLM", ) async def new_tool( tool_param1: str = Field(description="The description of the param1 for the LLM"), tool_param2: float = Field(description="The description of the param2 for the LLM") )-> str: """The new tool underlying method""" result = await some_api_call(tool_param1, tool_param2) return result ``` ### Adding New Resources To add a new resource, modify `main.py`: ```python @mcp.resource( uri="your-scheme://{param1}/{param2}", description="Description of what this resource provides", name="Your Resource Name", ) def your_resource(param1: str, param2: str) -> str: """The resource template implementation""" # Your resource logic here return f"Resource content for {param1} and {param2}" ``` The URI template uses `{param_name}` syntax to define parameters that will be extracted from the resource URI and passed to your function. ### Adding New Prompts To add a new prompt , modify `main.py`: ```python @mcp.prompt("") async def your_prompt( prompt_param: str = Field(description="The description of the param for the user") ) -> str: """Generate a helpful prompt""" return f"You are a friendly assistant, help the user and don't forget to {prompt_param}." ```