FastMCP Dog API Server 🐶

A production-ready Model Context Protocol (MCP) server built with Python, FastMCP, FastAPI, and HTTPX to provide dog breed data from the Dog API. Designed for seamless integration with AI assistants like Gemini, Claude, Cursor, and Antigravity IDE.

🌟 Live Endpoints

• Production SSE Endpoint: https://mcp-dog-api.vercel.app/sse

• Secondary SSE Endpoint: https://mcp-dog-api.vercel.app/mcp/sse

• GitHub Repository: https://github.com/rislrohitjain/mcp-dog-api

Related MCP server: Brandfetch MCP Server

✨ Features

• Dog Breeds Tool: Exposes the get_dog_breeds asynchronous tool to fetch real-time breed descriptions, attributes, and group data.

• Server-Sent Events (SSE): Full MCP SSE transport implementation for continuous streaming communication with AI clients.

• Cross-Origin Resource Sharing (CORS): Configured with edge-level and application-level CORS (Access-Control-Allow-Origin: *) for browser-based AI client handshakes.

• Gemini Spark OAuth Support: Includes auto-discovery (/.well-known/oauth-authorization-server), /authorize, and /token endpoints for zero-friction connection with Gemini Connected Apps.

• Cloud Native: Deployed serverless on Vercel with Python 3.12 runtime.

🛠️ Tech Stack

• Framework: FastMCP (mcp>=1.2.0,<2.0.0), FastAPI

• HTTP Client: httpx

• ASGI Server: Uvicorn

• Deployment: Vercel Serverless Functions (@vercel/python)

🚀 Local Development Setup

Prerequisites

• Python 3.10+

• Git

1. Clone Repository

git clone https://github.com/rislrohitjain/mcp-dog-api.git
cd mcp-dog-api

2. Create and Activate Virtual Environment

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

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

3. Install Dependencies

pip install -r requirements.txt

4. Run Server Locally

uvicorn api.index:app --host 0.0.0.0 --port 8000 --reload

• Local Status Endpoint: http://172.18.177.164:8000/

• Local SSE Endpoint: http://172.18.177.164:8000/mcp/sse

🔌 Connecting to AI Assistants

Gemini Connected Apps

1. Open Gemini -> Settings -> Connected Apps.

2. Add a Custom MCP Server and paste:

   https://mcp-dog-api.vercel.app/sse

3. Complete the auto-authorization step.

Local Agent Config (.agents/mcp_config.json)

{
  "mcpServers": {
    "dog-api-live": {
      "url": "https://mcp-dog-api.vercel.app/sse"
    }
  }
}

📂 Project Structure

mcp-dog-api/
├── api/
│   └── index.py            # FastMCP & FastAPI server implementation
├── .agents/
│   └── mcp_config.json     # MCP server configuration
├── requirements.txt        # Python package dependencies
├── vercel.json             # Vercel deployment & edge CORS configuration
├── .gitignore              # Git ignore rules
└── README.md               # Documentation

👤 Author & Profile

Created by Rohit Jain

• 🌐 Portfolio & Resume: https://rohitjain-resume.vercel.app/

• 🐙 GitHub: @rislrohitjain

• 📁 Repository: rislrohitjain/mcp-dog-api

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.