Content
# 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}."
```
MCP Store
