Barebones MCP Server

A minimal, dockerized template for creating HTTP-based Model Context Protocol (MCP) servers. This template provides a starting point for building your own MCP tools and services.

Features

HTTP Transport: Ready-to-use HTTP MCP server setup
Docker Support: Containerized deployment with Python 3.13
FastMCP Framework: Built using the FastMCP library for easy MCP server development
Template Structure: Clean, minimal codebase to build upon

What's Included

This template includes a simple get_cat_fact() tool as an example - replace it with your own tools and functionality.

Quick Start

Build and run the Docker container:

docker build -t mcp-server .
docker run -p 8000:8000 mcp-server

Connect in VS Code:
- Click the "Start" button on the .vscode/mcp.json file that appears in VS Code
- The MCP server will be automatically configured and connected
Access your tools:
- Open the Chat panel in VS Code
- Click the wrench icon (🔧) to see available MCP tools
- Your get_cat_fact() tool should appear and be ready to use
- Test with prompts like:
  - "Do you see a cat fact mcp tool?"
  - "Get me a cat fact"
- Note: The AI agent should prompt you that it will use an MCP tool before executing it. If you don't see this prompt, the tool isn't being used.

MCP Configuration

Add this to your .vscode/mcp.json file:

{
  "servers": {
    "my-mcp-tools": {
      "url": "http://localhost:8000/mcp"
    }
  }
}

Available Tools (Example)

get_cat_fact(): Example tool that returns a random cat fact - replace with your own tools

Debugging with MCP Inspector

For debugging and testing your MCP server, you can use the MCP Inspector:

Install and run MCP Inspector:
```
npx @modelcontextprotocol/inspector
```
Configure the connection:
- Set transport type to: httpstreamable
- Set URL to: http://localhost:8000/mcp
Test your tools:
- The inspector will show all available tools and their schemas
- You can test each tool directly from the web interface
- View server capabilities and debug any issues

Customizing the Template

Replace the example tool in server.py with your own MCP tools
Update dependencies in requirements.txt as needed
Modify the server name in the FastMCP constructor
Add your tool logic using the @mcp.tool() decorator

Quick Start

Build and run the Docker container:

docker build -t mcp-server .
docker run -p 8000:8000 mcp-server

Connect in VS Code:

Click the "Start" button on the .vscode/mcp.json file that appears in VS Code
The MCP server will be automatically configured and connected

Access your tools:

Open the Chat panel in VS Code
Click the wrench icon (🔧) to see available MCP tools
Your get_cat_fact() tool should appear and be ready to use
Test with prompts like:
- "Do you see a cat fact mcp tool?"
- "Get me a cat fact"
Note: The AI agent should prompt you that it will use an MCP tool before executing it. If you don't see this prompt, the tool isn't being used.

Debugging with MCP Inspector

For debugging and testing your MCP server, you can use the MCP Inspector:

Install and run MCP Inspector:

npx @modelcontextprotocol/inspector

Configure the connection:

Set transport type to: httpstreamable
Set URL to: http://localhost:8000/mcp

Test your tools:

The inspector will show all available tools and their schemas
You can test each tool directly from the web interface
View server capabilities and debug any issues