Azure DevOps Project Creator MCP server

Integrates with Azure DevOps to automate project creation and infrastructure setup through FastAPI, eliminating the need for manual portal interaction.
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Setup instructions
Provider
imghosty17
Release date
Mar 19, 2025
Language
Python

This MCP server (Model Context Protocol) provides a standardized API access layer for various AI models, offering a consistent interface for model interaction while handling authentication and monitoring. It simplifies working with different AI providers by providing a unified way to access their capabilities.

Installation

Prerequisites

  • Node.js 18 or higher
  • A MongoDB database (for persistent storage)
  • OPTIONAL: Redis (for caching)

Quick Start

  1. Clone the repository and install dependencies:
git clone https://github.com/yourusername/mcp-server-sandbox.git
cd mcp-server-sandbox
npm install
  1. Set up your environment variables by creating a .env file in the root directory:
# Server configuration
PORT=3000
NODE_ENV=development

# Database configuration
MONGODB_URI=mongodb://localhost:27017/mcp-server

# Optional Redis configuration
REDIS_URL=redis://localhost:6379

# Authentication (replace with your actual keys)
JWT_SECRET=your-secret-key
  1. Start the server:
npm start

Configuration

Basic Server Configuration

The server's behavior can be customized through the .env file. Key configurations include:

  • PORT: The port on which the server listens (default: 3000)
  • NODE_ENV: Environment setting (development, production, or test)
  • LOG_LEVEL: Logging verbosity (error, warn, info, http, debug)

Model Provider Setup

Edit the config/providers.js file to configure which AI model providers you want to enable:

module.exports = {
  openai: {
    enabled: true,
    apiKey: process.env.OPENAI_API_KEY,
    models: ['gpt-3.5-turbo', 'gpt-4']
  },
  anthropic: {
    enabled: true,
    apiKey: process.env.ANTHROPIC_API_KEY,
    models: ['claude-2', 'claude-instant-1']
  },
  // Add more providers as needed
}

Usage

API Endpoints

Authentication

POST /auth/login

Request body:

{
  "username": "user",
  "password": "password"
}

Response:

{
  "token": "your-jwt-token"
}

Model Completion

POST /v1/completions

Headers:

Authorization: Bearer your-jwt-token

Request body:

{
  "model": "gpt-3.5-turbo",
  "messages": [
    { "role": "system", "content": "You are a helpful assistant." },
    { "role": "user", "content": "Hello, how are you?" }
  ],
  "temperature": 0.7,
  "max_tokens": 100
}

Example: Using the API with cURL

# First authenticate
curl -X POST http://localhost:3000/auth/login \
  -H "Content-Type: application/json" \
  -d '{"username":"user","password":"password"}'

# Then make a request with the received token
curl -X POST http://localhost:3000/v1/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-jwt-token" \
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is the capital of France?"}
    ]
  }'

Example: Using the API with JavaScript

// Authentication
async function authenticate() {
  const response = await fetch('http://localhost:3000/auth/login', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ username: 'user', password: 'password' })
  });
  const data = await response.json();
  return data.token;
}

// Model completion
async function getCompletion(token) {
  const response = await fetch('http://localhost:3000/v1/completions', {
    method: 'POST',
    headers: { 
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${token}`
    },
    body: JSON.stringify({
      model: 'gpt-3.5-turbo',
      messages: [
        { role: 'system', content: 'You are a helpful assistant.' },
        { role: 'user', content: 'What is the capital of France?' }
      ]
    })
  });
  return await response.json();
}

// Usage
async function main() {
  const token = await authenticate();
  const completion = await getCompletion(token);
  console.log(completion);
}

Monitoring and Debugging

Logs

Server logs are available in the console and also written to the logs directory if enabled in your configuration. You can adjust the log level in your .env file:

LOG_LEVEL=debug

Health Check

The server provides a health endpoint to verify it's running correctly:

GET /health

Response:

{
  "status": "ok",
  "uptime": 3600,
  "version": "1.0.0"
}

How to install this MCP server

For Claude Code

To add this MCP server to Claude Code, run this command in your terminal:

claude mcp add-json "mcp-server-sandbox" '{"command":"npx","args":["-y","mcp-server-sandbox"]}'

See the official Claude Code MCP documentation for more details.

For Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > Tools & Integrations and click "New MCP Server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "mcp-server-sandbox": {
            "command": "npx",
            "args": [
                "-y",
                "mcp-server-sandbox"
            ]
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explicitly ask the agent to use the tool by mentioning the tool name and describing what the function does.

For Claude Desktop

To add this MCP server to Claude Desktop:

1. Find your configuration file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
  • Linux: ~/.config/Claude/claude_desktop_config.json

2. Add this to your configuration file:

{
    "mcpServers": {
        "mcp-server-sandbox": {
            "command": "npx",
            "args": [
                "-y",
                "mcp-server-sandbox"
            ]
        }
    }
}

3. Restart Claude Desktop for the changes to take effect

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