⚡ MCPify

Analyze any AI agent URL, detect framework capabilities, and generate ready-to-use Model Context Protocol (MCP) configurations. MCPify also exposes its own /mcp endpoint so it can itself be added as an MCP connector to any MCP client (Claude Desktop, Cursor, VS Code, etc.).

🚀 Features

• 🔍 Universal Agent URL Analysis: Probes common endpoints (/mcp, /sse, /health, /tools, /docs, /openapi.json) concurrently with HTTP heuristics.

• 🧠 Framework Detection: Identifies FastAPI, Flask, LangChain/LangGraph, Express, and Next.js agents with confidence scoring.

• ⚙️ Instant MCP Config Generator: Outputs ready-to-copy configuration JSON for:

  • Claude Desktop (mcpServers format)

  • Cursor & VS Code (SSE/Remote MCP format)

  • Remote MCP Endpoint

• 🔌 Self-Hosting MCP Server: Powered by fastapi-mcp, exposing its own /mcp endpoint and tools directly to AI clients.

• ⏱️ Zero-Downtime Free Tier Keep-Alive: Integrated APScheduler keep-alive worker pinging /health every 10 minutes to prevent Render/Koyeb sleeping.

• 🌐 Full CORS Support: Open for web frontends, browser extensions, and developer portals.

Related MCP server: agent-ready-mcp

📁 Project Structure

mcpify/
├── main.py              # FastAPI app entry point with FastApiMCP & APScheduler
├── requirements.txt     # Python dependencies
├── render.yaml          # Render deployment blueprint
├── .env.example         # Sample environment configuration
├── README.md            # Project documentation & guides
└── app/
    ├── __init__.py      # Package initialization
    ├── analyzer.py      # URL probing & framework detection heuristics
    ├── generator.py     # MCP configuration JSON generator
    └── mcp_handler.py   # MCP tools definition & REST router

🛠️ MCP Tools Exposed

When connected via /mcp, MCPify exposes three primary tools:

💻 Local Setup & Installation

1. Clone & Navigate

git clone <your-repo-url>
cd mcpify

2. Create Virtual Environment

# Windows
python -m venv venv
venv\Scripts\activate

# macOS / Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment

cp .env.example .env

Edit .env:

APP_URL=http://localhost:10000

5. Run the Server

uvicorn main:app --host 0.0.0.0 --port 10000 --reload

• Interactive Swagger API Docs: http://localhost:10000/docs

• MCP Endpoint: http://localhost:10000/mcp

• Health Check: http://localhost:10000/health

🚢 Deploying to Render

This project includes a preconfigured render.yaml for 1-click deployment on Render's free tier.

1. Push this repository to GitHub or GitLab.

2. Log into Render Dashboard.

3. Click New + > Blueprint.

4. Connect your repository.

5. Set APP_URL in the environment variables to your assigned Render URL (e.g. https://mcpify.onrender.com).

6. Deploy! The built-in APScheduler will automatically keep your service awake.

🔌 Connecting MCPify to Your Client

1. Claude Desktop

Add MCPify to claude_desktop_config.json:

{
  "mcpServers": {
    "mcpify": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://your-mcpify-url.onrender.com/mcp"
      ]
    }
  }
}

2. Cursor / VS Code

In Cursor Settings > Features > MCP > Add New MCP Server:

• Name: mcpify

• Type: sse

• URL: https://your-mcpify-url.onrender.com/mcp

📡 API Endpoints

1. Health Check

GET /health

{
  "status": "ok"
}

2. Analyze Agent URL

POST /analyze

{
  "url": "https://sample-agent.onrender.com"
}

Response:

{
  "url": "https://sample-agent.onrender.com",
  "detected_framework": "FastAPI",
  "confidence_score": 0.95,
  "available_endpoints": [
    {
      "path": "/health",
      "status_code": 200,
      "accessible": true,
      "content_type": "application/json"
    },
    {
      "path": "/docs",
      "status_code": 200,
      "accessible": true,
      "content_type": "text/html; charset=utf-8"
    }
  ],
  "recommended_mcp_endpoint": "https://sample-agent.onrender.com/mcp",
  "details": {
    "signals": [
      "Server header contains 'uvicorn'",
      "OpenAPI specification available at /openapi.json",
      "Swagger UI documentation available at /docs"
    ],
    "framework_scores": {
      "FastAPI": 0.95,
      "Flask": 0.0,
      "LangChain": 0.0,
      "Express": 0.0,
      "Next.js": 0.0
    }
  }
}

3. Generate MCP Config

POST /generate

{
  "url": "https://sample-agent.onrender.com"
}

4. Integration Guide

POST /guide

{
  "url": "https://sample-agent.onrender.com",
  "platform": "claude_desktop"
}

📜 License

MIT License. Built for seamless AI agent interoperability with Model Context Protocol.