Which integrations are available for this server?

Uses Flask as the web framework to serve the MCP protocol endpoints, web interface, and Server-Sent Events streaming capabilities. Supports deployment with Gunicorn for production environments with options for binding, port reuse, and hot reloading. Provides integration with Azure OpenAI services for LLM completions, supporting both streaming and non-streaming responses with configurable models and deployments. Includes metrics collection for monitoring request counts, response times, and error rates through a dedicated /metrics endpoint. Implements Pydantic models for MCP protocol validation and type safety throughout the application.

How do I use 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., "@MCP Server summarize the main features of this MCP server" 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.

MCP Server - Model Context Protocol Implementation

A comprehensive Python backend implementing the Model Context Protocol (MCP) with JSON-RPC 2.0, Azure OpenAI integration, and Server-Sent Events streaming capabilities.

Features

Complete MCP Protocol Support: JSON-RPC 2.0 compliant implementation
Azure OpenAI Integration: Seamless connection to Azure OpenAI services
Streaming Responses: Real-time streaming via Server-Sent Events (SSE)
Resource Management: File system resource discovery and access
Tool Execution: Extensible tool registry with validation
Authentication: JWT-based authentication system
Monitoring: Prometheus metrics collection
Web Interface: Built-in testing and management interface

Related MCP server: MCP Python SDK

Architecture

├── app/
│   ├── core/
│   │   ├── config.py          # Configuration management
│   │   ├── errors.py          # Custom exception classes
│   │   └── logging.py         # Structured logging setup
│   ├── protocol/
│   │   ├── enums.py           # MCP protocol enumerations
│   │   └── models.py          # Pydantic models for MCP
│   ├── services/
│   │   ├── llm.py             # Azure OpenAI service
│   │   ├── resources.py       # Resource management
│   │   └── tools.py           # Tool registry and execution
│   ├── transport/
│   │   └── http.py            # HTTP transport layer
│   ├── auth.py                # JWT authentication
│   └── metrics.py             # Prometheus metrics
├── static/
│   └── app.js                 # Frontend JavaScript
├── templates/
│   └── index.html             # Web interface
├── main.py                    # Application entry point
└── server.py                  # Flask app configuration

Installation

Clone the repository:

git clone <repository-url>
cd mcp-server

Install dependencies:

pip install -r requirements.txt

Set up environment variables:

# Required for Azure OpenAI
export OPENAI_API_KEY="your-azure-openai-api-key"
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com"
export AZURE_OPENAI_DEPLOYMENT="your-deployment-name"
export AZURE_OPENAI_API_VERSION="2024-08-01-preview"

# Optional configurations
export JWT_SECRET="your-jwt-secret"
export SESSION_SECRET="your-session-secret"

Configuration

The server supports both Azure OpenAI and standard OpenAI configurations:

Azure OpenAI (Recommended)

USE_AZURE_OPENAI = True
AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com"
AZURE_OPENAI_DEPLOYMENT = "gpt-4o"
AZURE_OPENAI_API_VERSION = "2024-08-01-preview"

Standard OpenAI

USE_AZURE_OPENAI = False
OPENAI_MODEL = "gpt-4o"

Running the Server

Development

python main.py

Production

gunicorn --bind 0.0.0.0:5000 --reuse-port --reload main:app

The server will be available at http://localhost:5000

API Endpoints

MCP Protocol

POST /rpc - JSON-RPC 2.0 endpoint for MCP requests
GET /events - Server-Sent Events for streaming responses

Management

GET / - Web interface for testing and management
GET /health - Health check endpoint
GET /metrics - Prometheus metrics

Authentication

The server uses JWT-based authentication. Include the token in requests:

# HTTP Headers
Authorization: Bearer <token>

# Query Parameters (for SSE)
?token=<token>

Default development token: devtoken

MCP Protocol Support

Capabilities

Resources: File system resource discovery and reading
Tools: Extensible tool execution with validation
Sampling: LLM completion requests (streaming and non-streaming)
Logging: Structured JSON logging

Example Requests

Initialize Connection

{
  "jsonrpc": "2.0",
  "id": "init",
  "method": "initialize",
  "params": {
    "protocolVersion": "2024-11-05",
    "capabilities": {},
    "clientInfo": {"name": "test-client", "version": "1.0.0"}
  }
}

List Resources

{
  "jsonrpc": "2.0",
  "id": "resources",
  "method": "resources/list",
  "params": {}
}

Execute Tool

{
  "jsonrpc": "2.0",
  "id": "tool",
  "method": "tools/call",
  "params": {
    "name": "calculate",
    "arguments": {"operation": "add", "a": 5, "b": 3}
  }
}

LLM Completion

{
  "jsonrpc": "2.0",
  "id": "completion",
  "method": "sampling/createMessage",
  "params": {
    "messages": [{"content": {"type": "text", "text": "Hello, world!"}}],
    "maxTokens": 100
  }
}

Extending the Server

Adding New Tools

from app.services.tools import mcp_tool

@mcp_tool("my_tool", {
    "type": "object",
    "properties": {
        "param1": {"type": "string"},
        "param2": {"type": "number"}
    },
    "required": ["param1"]
})
async def my_custom_tool(param1: str, param2: float = 0.0):
    """Custom tool implementation"""
    return {"result": f"Processed {param1} with {param2}"}

Custom Resource Handlers

from app.services.resources import ResourceService

class CustomResourceService(ResourceService):
    async def list_resources(self, base_path: str = "."):
        # Custom resource discovery logic
        pass

Monitoring

The server includes comprehensive monitoring:

Prometheus Metrics: Request counts, response times, error rates
Structured Logging: JSON-formatted logs with correlation IDs
Health Checks: Application and dependency status

Security

Environment-based configuration (no hardcoded secrets)
JWT authentication with configurable secrets
Input validation on all endpoints
Rate limiting headers from Azure OpenAI

Development

Running Tests

# Test the API endpoints
curl -X POST http://localhost:5000/rpc \
  -H "Authorization: Bearer devtoken" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":"test","method":"initialize","params":{}}'

# Test streaming
curl -N "http://localhost:5000/events?token=devtoken&prompt=Hello&stream=true"

Adding Dependencies

pip install <package-name>
pip freeze > requirements.txt

Troubleshooting

Common Issues

Azure OpenAI Connection Errors
- Verify AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_DEPLOYMENT
- Check API key permissions
- Ensure correct API version
Authentication Failures
- Verify JWT token format
- Check token expiration
- Ensure correct secret configuration
Streaming Issues
- Use query parameters for SSE authentication
- Check network connectivity for long-running streams

Debug Logging

Enable debug logging by setting:

export DEBUG=true

License

This project is licensed under the MIT License.

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests for new functionality
Submit a pull request

Support

For issues and questions:

Check the troubleshooting section
Review the API documentation
Open an issue on GitHub

MCP Server

MCP Server - Model Context Protocol Implementation

Features

Architecture

Installation

Configuration

Azure OpenAI (Recommended)

Standard OpenAI

Running the Server

Development

Production

API Endpoints

MCP Protocol

Management

Authentication

MCP Protocol Support

Capabilities

Example Requests

Initialize Connection

List Resources

Execute Tool

LLM Completion

Extending the Server

Adding New Tools

Custom Resource Handlers

Monitoring

Security

Development

Running Tests

Adding Dependencies

Troubleshooting

Common Issues

Debug Logging

License

Contributing

Support

Resources

Appeared in Searches

Latest Blog Posts

MCP directory API