ChiragPatankar AI Customer Support Bot MCP Server

WHAT IT CONNECTS

What ChiragPatankar AI Customer Support Bot MCP Server gives an AI Agent

• Real-time context fetching from Glama.ai
• AI-powered response generation with Cursor AI
• Batch processing support
• Priority queuing
• Rate limiting
• User interaction tracking
• Health monitoring
• MCP protocol compliance
• Python 3.8+
• PostgreSQL database
• Glama.ai API key
• Cursor AI API key
• Clone the repository:
• Create and activate a virtual environment:
• Install dependencies:
• Create a `.env` file based on `.env.example`:
• Configure your `.env` file with your credentials:
• Set up the database:
• 100 requests per 60 seconds
• Rate limit information is included in the health check endpoint
• Rate limit exceeded responses include reset time
• `RATE_LIMIT_EXCEEDED`: Rate limit exceeded
• `UNSUPPORTED_MCP_VERSION`: Unsupported MCP version
• `PROCESSING_ERROR`: Error processing request
• `CONTEXT_FETCH_ERROR`: Error fetching context from Glama.ai
• `BATCH_PROCESSING_ERROR`: Error processing batch request
• Update `mcp_config.py` with new configuration options
• Add new models in `models.py` if needed
• Create new endpoints in `app.py`
• Update capabilities endpoint to reflect new features
• All MCP endpoints require authentication via `X-MCP-Auth` header
• Rate limiting is implemented to prevent abuse
• Database credentials should be kept secure
• API keys should never be committed to version control
• Service status
• Rate limit usage
• Connected services
• Processing times
• Fork the repository
• Create a feature branch
• Commit your changes
• Push to the branch
• Create a Pull Request