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AI Customer Support Bot - MCP Server

@MCP-Mirror

代码仓库

基本信息

分类

数据与分析

许可证

MIT license

传输方式

stdio

作者

MCP-Mirror

关于 AI Customer Support Bot - MCP Server

Mirror of

#ai#customer-support#mcp-server

AI Customer Support Bot - MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

AI Customer Support Bot - MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

1

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.

2

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`.

3

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.

4

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.

5

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.

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