Which integrations are available for this server?

Provides containerized deployment options using Docker and Docker Compose for easy setup and operation of the MCP server. Implements a streamable HTTP MCP server using FastAPI with Server-Sent Events (SSE) support for real-time communication and response streaming. Integrates with Azure OpenAI GPT-4o for intelligent tool usage, enabling capabilities like calculations, weather queries, and time-related functions through tool calling features.

How do I use Streamable HTTP MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Streamable HTTP MCP Server calculate the total cost for 5 items at $12.99 each" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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

[GPT-4o Client] <--HTTP--> [MCP Server] <--SSE--> [Tools]
     |                         |
     |                    [Calculator]
     |                    [Weather]
     |                    [Time]

🌐 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