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

Deploy on Alpic

Prerequisites

• Install uv (https://docs.astral.sh/uv/getting-started/installation/)

Installation

1. Clone the repository:

git clone [email protected]:alpic-ai/mcp-server-template-python.git
cd mcp-server-template-python

2. Install python version & dependencies:

uv python install
uv sync --locked

Usage

Start the server on port 3000:

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:

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:

@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:

@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:

@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}."