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AI Customer Support Bot - MCP Server

@ChiragPatankar

代码仓库

基本信息

分类

数据与分析

许可证

MIT

传输方式

stdio

作者

ChiragPatankar

关于 AI Customer Support Bot - MCP Server

暂无概览

#ai-customer-support#mcp-server#customer-support-bot

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": {
    "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?

A Model Context Protocol (MCP) compliant server framework built with Python, FastAPI, and PostgreSQL. It lets developers create intelligent, AI-powered customer support systems without vendor lock-in. Clean architecture with layered design for production readiness.

How to use AI Customer Support Bot - MCP Server?

Clone the repository, create a virtual environment with Python 3.8+, install dependencies from requirements.txt, copy and edit .env.example to configure your database and AI service credentials, then run python app.py. The server starts at http://localhost:8000.

Key features of AI Customer Support Bot - MCP Server

  • Full MCP protocol implementation
  • Production-ready with auth, rate limiting, and monitoring
  • High performance with FastAPI async support
  • AI-agnostic – integrate any provider (OpenAI, Anthropic, etc.)
  • Batch processing for multiple queries
  • Secure by default: token auth, input validation, audit logging

Use cases of AI Customer Support Bot - MCP Server

  • Automate customer support queries with AI-generated responses
  • Process high volumes of support tickets via batch API
  • Build a vendor-independent support bot that can switch AI providers
  • Monitor and scale support system with built-in health metrics

常见问题

1

What is MCP?

MCP stands for Model Context Protocol. This server implements the full MCP specification for interoperability with AI services.

2

What are the runtime requirements?

Python 3.8+, a PostgreSQL database, and credentials for an AI service (e.g., OpenAI, Anthropic).

3

How do I add my own AI provider?

Install the provider’s SDK, add its API key and model to your `.env` file, then implement a service class that generates responses from the AI model.

4

How is authentication handled?

The server uses token-based authentication passed via the `X-MCP-Auth` header on all API requests.

5

Does the server support scaling?

Yes. For production, use connection pooling, add Redis for distributed rate limiting, and deploy behind a load balancer. Docker support is coming soon.

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