AI Customer Support Bot - MCP Server
by ChiragPatankar
An MCP server providing AI-powered customer support by integrating Cursor AI and Glama.ai. It offers real-time context fetching and AI-driven response generation for efficient customer interactions.
Last updated: N/A
What is AI Customer Support Bot - MCP Server?
This is a Model Context Protocol (MCP) server designed to provide AI-powered customer support. It leverages the capabilities of Cursor AI for response generation and Glama.ai for real-time context fetching, enabling intelligent and context-aware customer interactions.
How to use AI Customer Support Bot - MCP Server?
To use this server, you need to clone the repository, set up the required environment variables (API keys, database URL), install dependencies, and run the app.py script. The server exposes several API endpoints for processing single queries, batch queries, checking server health, and retrieving server capabilities. Authentication is required via the X-MCP-Auth header.
Key features of AI Customer Support Bot - MCP Server
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
Use cases of AI Customer Support Bot - MCP Server
Automated customer support
Intelligent chatbot integration
Context-aware query answering
High-priority issue resolution
Scalable customer interaction management
FAQ from AI Customer Support Bot - MCP Server
What is the purpose of the X-MCP-Auth header?
What is the purpose of the X-MCP-Auth header?
The X-MCP-Auth header is used for authentication and authorization. All MCP endpoints require this header to ensure secure access and prevent unauthorized usage.
How do I configure the database connection?
How do I configure the database connection?
The database connection is configured using the DATABASE_URL environment variable. This variable should contain the connection string for your PostgreSQL database.
What happens when the rate limit is exceeded?
What happens when the rate limit is exceeded?
When the rate limit is exceeded, the server returns a 429 Too Many Requests error with a JSON response containing the error code RATE_LIMIT_EXCEEDED and the time until the rate limit resets.
How do I add new features to the server?
How do I add new features to the server?
To add new features, you should update the mcp_config.py file with new configuration options, add new models in models.py if needed, create new endpoints in app.py, and update the capabilities endpoint to reflect the new features.
What is the purpose of the health check endpoint?
What is the purpose of the health check endpoint?
The health check endpoint provides information about the server's status, including service status, rate limit usage, connected services, and processing times. It is used for monitoring the server's health and performance.