iflow-mcp_alpic-ai-mcp-server-template 0.1.0

Python MCP Server template

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

Prerequisites

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

Installation

1. Clone the repository:

git clone [e-mail]: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}."