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Krishanu-das-05

MCP Server

by Krishanu-das-05
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

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

  1. Clone the repository:

git clone <repository-url> cd mcp-server

  1. Install dependencies:

pip install -r requirements.txt

  1. 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

  1. Azure OpenAI Connection Errors

    • Verify AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_DEPLOYMENT

    • Check API key permissions

    • Ensure correct API version

  2. Authentication Failures

    • Verify JWT token format

    • Check token expiration

    • Ensure correct secret configuration

  3. 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

  1. Fork the repository

  2. Create a feature branch

  3. Make your changes

  4. Add tests for new functionality

  5. Submit a pull request

Support

For issues and questions:

  • Check the troubleshooting section

  • Review the API documentation

  • Open an issue on GitHub

This server cannot be installed

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security - not tested
A
license - permissive license
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quality - not tested

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