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AI Customer Support Bot - MCP Server

@MCP-Mirror

Repository

About AI Customer Support Bot - MCP Server

Mirror of

#ai#customer-support#mcp-server

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "ChiragPatankar_AI-Customer-Support-Bot--MCP-Server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is AI Customer Support Bot - MCP Server?

AI Customer Support Bot - MCP Server is a Model Context Protocol (MCP) server that provides AI-powered customer support by integrating with Cursor AI and Glama.ai. It is designed for developers and support teams who need real-time context fetching, batch processing, and intelligent response generation within an MCP-compliant framework.

How to use AI Customer Support Bot - MCP Server?

Install Python 3.8+, PostgreSQL, and obtain API keys for Glama.ai and Cursor AI. Clone the repository, set up a virtual environment, install dependencies, copy .env.example to .env and fill in credentials, create the database, run migrations, then start the server with python app.py. The server runs on http://localhost:8000 and exposes endpoints like POST /mcp/process for single queries and POST /mcp/batch for batch processing.

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 and priority queuing
  • Rate limiting (100 requests per 60 seconds)
  • Health monitoring and error tracking
  • MCP protocol compliance

Use cases of AI Customer Support Bot - MCP Server

  • Automatically answering customer inquiries (e.g., password reset instructions)
  • Processing multiple support tickets in a single batch request
  • Monitoring server health and rate limit usage in production
  • Integrating Glama.ai and Cursor AI into an existing MCP workflow
  • Handling high-priority requests with priority queuing

FAQ from AI Customer Support Bot - MCP Server

What are the prerequisites to run the server?

You need Python 3.8+, a PostgreSQL database, and valid API keys for Glama.ai and Cursor AI.

How do I configure environment variables?

Copy .env.example to .env and set your Glama API key, Cursor API key, database URL, secret key, and server parameters like MAX_CONTEXT_RESULTS and RATE_LIMIT_REQUESTS.

Which API endpoints are available?

The server provides GET / (root info), GET /mcp/version, GET /mcp/capabilities, POST /mcp/process, POST /mcp/batch, and GET /mcp/health. All MCP endpoints require an X-MCP-Auth header.

How does rate limiting work?

By default, the server allows 100 requests per 60 seconds. Rate limit usage is exposed via the health endpoint, and exceeded requests return a RATE_LIMIT_EXCEEDED error with reset time.

What error codes does the server return?

Common error codes include RATE_LIMIT_EXCEEDED, UNSUPPORTED_MCP_VERSION, PROCESSING_ERROR, CONTEXT_FETCH_ERROR, and BATCH_PROCESSING_ERROR. All errors follow a structured JSON format.

Frequently asked questions

What are the prerequisites to run the server?

You need Python 3.8+, a PostgreSQL database, and valid API keys for Glama.ai and Cursor AI.

How do I configure environment variables?

Copy `.env.example` to `.env` and set your Glama API key, Cursor API key, database URL, secret key, and server parameters like `MAX_CONTEXT_RESULTS` and `RATE_LIMIT_REQUESTS`.

Which API endpoints are available?

The server provides `GET /` (root info), `GET /mcp/version`, `GET /mcp/capabilities`, `POST /mcp/process`, `POST /mcp/batch`, and `GET /mcp/health`. All MCP endpoints require an `X-MCP-Auth` header.

How does rate limiting work?

By default, the server allows 100 requests per 60 seconds. Rate limit usage is exposed via the health endpoint, and exceeded requests return a `RATE_LIMIT_EXCEEDED` error with reset time.

What error codes does the server return?

Common error codes include `RATE_LIMIT_EXCEEDED`, `UNSUPPORTED_MCP_VERSION`, `PROCESSING_ERROR`, `CONTEXT_FETCH_ERROR`, and `BATCH_PROCESSING_ERROR`. All errors follow a structured JSON format.

Comments

Basic information

Category

Data & Analytics

License

MIT license

Transports

stdio

Author

MCP-Mirror

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