AI Customer Support Bot - MCP Server

Name: AI Customer Support Bot - MCP Server
Author: MCP-Mirror

@MCP-Mirror

リポジトリ

AI Customer Support Bot - MCP Server について

Mirror of

#ai #customer-support #mcp-server

基本情報

カテゴリ

データと分析

ライセンス

MIT license

ランタイム

python

トランスポート

stdio

公開者

MCP-Mirror

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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.

「データと分析」の他のコンテンツ

🪐✨ Jupyter MCP Server

datalayer

🪐 🔧 Model Context Protocol (MCP) Server for Jupyter.

1.2K

MCP Deep Web Research Server (v0.3.0)

qpd-v

Enhanced MCP server for deep web research

arxiv-latex MCP Server

takashiishida

MCP server that uses arxiv-to-prompt to fetch and process arXiv LaTeX sources for precise interpretation of mathematical expressions in scientific papers.

140