MCP Connect

Qu'est-ce que MCP Connect ?

MCP Connect est un outil qui permet aux services d'IA basés sur le cloud d'accéder aux serveurs locaux utilisant le protocole de contexte de modèle (MCP) basé sur Stdio, comblant ainsi le fossé entre les ressources locales et les applications cloud.

Comment utiliser MCP Connect ?

Pour utiliser MCP Connect :

• Clonez le dépôt
• Configurez les variables d'environnement
• Installez les dépendances
• Exécutez l'application localement
• Optionnellement, exécutez-la avec un tunnel (par exemple, Ngrok) pour l'accessibilité cloud

Caractéristiques clés de MCP Connect

• Intégration Cloud : Connecte les outils d'IA cloud avec les serveurs MCP locaux
• Traduction de Protocole : Convertit les requêtes HTTP/HTTPS en communication Stdio
• Sécurité : Assure un accès sécurisé aux ressources locales
• Flexibilité : Prend en charge divers serveurs MCP sans modification
• Facilité d'utilisation : Ne nécessite aucun changement sur le serveur MCP
• Support de Tunnel : Support intégré pour le tunnel Ngrok

Cas d'utilisation de MCP Connect

• Intégration d'outils d'IA locaux avec des applications basées sur le cloud
• Accès sécurisé aux ressources locales depuis des environnements cloud
• Facilitation de la communication entre les services cloud et les serveurs MCP locaux

FAQ sur MCP Connect

MCP Connect est-il facile à configurer ?

Oui ! Il nécessite une configuration minimale et peut être mis en place rapidement.

Puis-je utiliser MCP Connect avec n'importe quel serveur MCP ?

Oui ! MCP Connect est conçu pour fonctionner avec divers serveurs MCP sans nécessiter de modifications.

Quelles sont les conditions préalables pour utiliser MCP Connect ?

Vous devez avoir Node.js installé pour exécuter MCP Connect.

MCP Connect

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The Model Context Protocol (MCP) introduced by Anthropic is cool. However, most MCP servers are built on Stdio transport, which, while excellent for accessing local resources, limits their use in cloud-based applications.

MCP Connect is a tiny tool that is created to solve this problem:

• Cloud Integration: Enables cloud-based AI services to interact with local Stdio based MCP servers
• Protocol Translation: Converts HTTP/HTTPS requests to Stdio communication
• Security: Provides secure access to local resources while maintaining control
• Flexibility: Supports various MCP servers without modifying their implementation
• Easy to use: Just run MCP Connect locally, zero modification to the MCP server
• Tunnel: Built-in support for Ngrok tunnel

By bridging this gap, we can leverage the full potential of local MCP tools in cloud-based AI applications without compromising on security.

How it works

+-----------------+     HTTPS/SSE      +------------------+      stdio      +------------------+
|                 |                    |                  |                 |                  |
|  Cloud AI tools | <--------------->  |  Node.js Bridge  | <------------>  |    MCP Server    |
|   (Remote)      |       Tunnels      |    (Local)       |                 |     (Local)      |
|                 |                    |                  |                 |                  |
+-----------------+                    +------------------+                 +------------------+

Prerequisites

• Node.js

Quick Start

1. Clone the repository

   git clone https://github.com/EvalsOne/MCP-connect.git
   

   and enter the directory

   cd MCP-connect
   

2. Copy .env.example to .env and configure the port and auth_token:

   cp .env.example .env
   

3. Install dependencies:

   npm install
   

4. Run MCP Connect

   # build MCP Connect
   npm run build
   # run MCP Connect
   npm run start
   # or, run in dev mode (supports hot reloading by nodemon)
   npm run dev
   

Now MCP connect should be running on http://localhost:3000/bridge.

Note:

• The bridge is designed to be run on a local machine, so you still need to build a tunnel to the local MCP server that is accessible from the cloud.
• Ngrok, Cloudflare Zero Trust, and LocalTunnel are recommended for building the tunnel.

Running with Ngrok Tunnel

MCP Connect has built-in support for Ngrok tunnel. To run the bridge with a public URL using Ngrok:

1. Get your Ngrok auth token from https://dashboard.ngrok.com/authtokens
2. Add to your .env file:

   NGROK_AUTH_TOKEN=your_ngrok_auth_token
   

3. Run with tunnel:

   # Production mode with tunnel
   npm run start:tunnel
   
   # Development mode with tunnel
   npm run dev:tunnel
   

After MCP Connect is running, you can see the MCP bridge URL in the console.

API Endpoints

After MCP Connect is running, there are two endpoints exposed:

• GET /health: Health check endpoint
• POST /bridge: Main bridge endpoint for receiving requests from the cloud

For example, the following is a configuration of the official GitHub MCP:

{
  "command": "npx",
  "args": [
    "-y",
    "@modelcontextprotocol/server-github"
  ],
  "env": {
    "GITHUB_PERSONAL_ACCESS_TOKEN": "<your_github_personal_access_token>"
  }
}

You can send a request to the bridge as the following to list the tools of the MCP server and call a specific tool.

Listing tools:

curl -X POST http://localhost:3000/bridge \
     -d '{
       "method": "tools/list",
       "serverPath": "npx",
       "args": [
         "-y",
         "@modelcontextprotocol/server-github"
       ],
       "params": {},
       "env": {
         "GITHUB_PERSONAL_ACCESS_TOKEN": "<your_github_personal_access_token>"
       }
     }'

Calling a tool:

Using the search_repositories tool to search for repositories related to modelcontextprotocol

curl -X POST http://localhost:3000/bridge \
     -d '{
       "method": "tools/call",
       "serverPath": "npx",
       "args": [
         "-y",
         "@modelcontextprotocol/server-github"
       ],
       "params": {
         "name": "search_repositories",
         "arguments": {
            "query": "modelcontextprotocol"
         },
       },
       "env": {
         "GITHUB_PERSONAL_ACCESS_TOKEN": "<your_github_personal_access_token>"
       }
     }'

Authentication

MCP Connect uses a simple token-based authentication system. The token is stored in the .env file. If the token is set, MCP Connect will use it to authenticate the request.

Sample request with token:

curl -X POST http://localhost:3000/bridge \
     -H "Authorization: Bearer <your_auth_token>" \
     -d '{
       "method": "tools/list",
       "serverPath": "npx",
       "args": [
         "-y",
         "@modelcontextprotocol/server-github"
       ],
       "params": {},
       "env": {
         "GITHUB_PERSONAL_ACCESS_TOKEN": "<your_github_personal_access_token>"
       }
     }'

Configuration

Required environment variables:

• AUTH_TOKEN: Authentication token for the bridge API (Optional)
• PORT: HTTP server port (default: 3000, required)
• LOG_LEVEL: Logging level (default: info, required)
• NGROK_AUTH_TOKEN: Ngrok auth token (Optional)

Using MCP Connect with ConsoleX AI to access local MCP Server

The following is a demo of using MCP Connect to access a local MCP Server on ConsoleX AI:

License

MIT License