AI Customer Support Bot - MCP Server

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

@MCP-Mirror

代码仓库

关于 AI Customer Support Bot - MCP Server

Mirror of

#ai #customer-support #mcp-server

基本信息

分类

数据与分析

许可证

MIT license

运行时

python

传输方式

stdio

发布者

MCP-Mirror

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"
      ]
    }
  }
}

工具

未检测到工具

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

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.

数据与分析分类下的更多 MCP 服务器

PubMed MCP Server

cyanheads

Search PubMed/Europe PMC, fetch articles and full text (PMC/EPMC/Unpaywall), citations, MeSH terms via MCP. STDIO or Streamable HTTP.

121

Federal Reserve Economic Data MCP Server

stefanoamorelli

Open-source FRED MCP Server (Federal Reserve Economic Data)

104

🎓 Semantic Scholar MCP Server

JackKuo666

🔍 This project implements a Model Context Protocol (MCP) server for interacting with the Semantic Scholar API. It provides tools for searching papers, retrieving paper and author details, and fetching citations and references.