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Streamable HTTP MCP Server

by tevinric
GitHub
Python
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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

📋 Prerequisites

  • Docker and Docker Compose

  • Azure OpenAI account with GPT-4o deployment

  • Python 3.11+ (for local development)

🛠️ Quick Setup

  1. Clone and setup:

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

  1. 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

  1. Start the MCP server:

./setup.sh start

  1. 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

  1. Calculator: Evaluate mathematical expressions

  2. Weather: Get mock weather data for any location

  3. 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

  1. Create a new tool class in mcp_server.py

  2. Add tool definition to TOOLS dictionary

  3. 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

  1. Connection refused: Make sure MCP server is running on port 8000

  2. Authentication errors: Check your Azure OpenAI credentials in .env

  3. 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.

This server cannot be installed

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security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

Implements a Model Context Protocol server that enables streaming communication between Azure OpenAI GPT-4o and tool services, allowing for real-time intelligent tool usage via Server-Sent Events.

  1. 🚀 Features
    1. 📋 Prerequisites
      1. 🛠️ Quick Setup
        1. 📖 Usage Examples
          1. Basic Tool Usage
          2. Complex Multi-tool Usage
        2. 🔧 Available Tools
          1. 🏗️ Architecture
            1. ���� API Endpoints
              1. 📝 Manual Testing
                1. 🐳 Docker Commands
                  1. 🧪 Development
                    1. Local Development
                    2. Adding New Tools
                  2. 🐛 Troubleshooting
                    1. Common Issues
                    2. Debug Mode
                  3. 🔒 Security Notes
                    1. 📄 License

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