Streamable HTTP MCP Server with Azure OpenAI GPT-4o

This project implements a Streamable HTTP MCP (Model Context Protocol) Server using FastAPI and integrates it with Azure OpenAI GPT-4o for intelligent tool usage.

🚀 Features

MCP Server: Streamable HTTP server with SSE support
Azure OpenAI Integration: GPT-4o with tool calling capabilities
Simple Tools: Calculator, Weather (mock), and Time tools
Docker Setup: Easy deployment with docker-compose
Real-time Communication: Server-Sent Events (SSE) for streaming responses

Related MCP server: Azure DevOps MCP Server

📋 Prerequisites

Docker and Docker Compose
Azure OpenAI account with GPT-4o deployment
Python 3.11+ (for local development)

🛠️ Quick Setup

Clone and setup:

# Copy environment file cp .env.example .env # Edit .env with your Azure OpenAI credentials nano .env

Configure Azure OpenAI:

AZURE_OPENAI_API_KEY=your_api_key_here AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/ AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o AZURE_OPENAI_API_VERSION=2024-02-01

Start the MCP server:

./setup.sh start

Run the GPT-4o client:

./setup.sh client

📖 Usage Examples

Basic Tool Usage

The client automatically demonstrates various tool interactions:

Query: What's the current time? Response: The current time is 2024-01-15T14:30:45.123456 Query: Calculate 15 * 42 + 33 Response: The result is 663 Query: What's the weather like in New York? Response: The weather in New York is currently sunny with a temperature of 22°C...

Complex Multi-tool Usage

Query: Can you get the weather for London and then calculate the percentage if the temperature was 20 degrees and now it's 25 degrees? Response: The weather in London is currently 22°C and sunny... The percentage increase from 20°C to 25°C is 25%.

🔧 Available Tools

Calculator: Evaluate mathematical expressions
Weather: Get mock weather data for any location
Time: Get current timestamp

🏗️ Architecture

🌐 API Endpoints

POST /sse - Main MCP communication endpoint
GET /health - Health check
GET /tools - List available tools

📝 Manual Testing

Test the MCP server directly:

# Health check curl http://localhost:8000/health # List tools curl http://localhost:8000/tools # Test SSE endpoint curl -X POST http://localhost:8000/sse \ -H "Content-Type: application/json" \ -d '{"jsonrpc": "2.0", "id": "1", "method": "tools/list", "params": {}}'

🐳 Docker Commands

# Start MCP server only docker-compose up -d mcp-server # Run client once docker-compose run --rm client # View logs docker-compose logs -f mcp-server # Stop everything docker-compose down

🧪 Development

Local Development

# Install dependencies pip install -r requirements.txt # Run server locally python mcp_server.py # Run client locally (in another terminal) source .env python client.py

Adding New Tools

Create a new tool class in mcp_server.py
Add tool definition to TOOLS dictionary
Add handler in MCPHandler.handle_tools_call

Example:

class NewTool: @staticmethod def do_something(param: str) -> Dict[str, Any]: return {"result": f"Processed: {param}"} # Add to TOOLS dictionary TOOLS["new_tool"] = { "name": "new_tool", "description": "Does something useful", "inputSchema": { "type": "object", "properties": { "param": {"type": "string", "description": "Input parameter"} }, "required": ["param"] } }

🐛 Troubleshooting

Common Issues

Connection refused: Make sure MCP server is running on port 8000
Authentication errors: Check your Azure OpenAI credentials in .env
Tool call failures: Check MCP server logs for detailed error messages

Debug Mode

Enable debug logging:

docker-compose logs -f mcp-server

🔒 Security Notes

Never commit your .env file with real credentials
Use environment variables in production
Consider adding authentication for production deployments

📄 License

MIT License - feel free to use and modify as needed.

Streamable HTTP MCP Server