AI Customer Support Bot - MCP Server

by ChiragPatankar

4 stars
207 downloads
Not rated
GitHub

Description

# πŸ€– AI Customer Support Bot - MCP Server <div align="center"> ![Python](https://img.shields.io/badge/python-v3.8+-blue.svg) ![FastAPI](https://img.shields.io/badge/FastAPI-005571?style=flat&logo=fastapi)…

About

# πŸ€– AI Customer Support Bot - MCP Server <div align="center"> ![Python](https://img.shields.io/badge/python-v3.8+-blue.svg) ![FastAPI](https://img.shields.io/badge/FastAPI-005571?style=flat&logo=fastapi) ![PostgreSQL](https://img.shields.io/badge/PostgreSQL-316192?style=flat&logo=postgresql&logoColor=white)…

Details

Author
ChiragPatankar
GitHub stars
4
Downloads
207
Categories
Search

- Clean architecture with clear separation of concerns
- Full MCP (Model Context Protocol) compliance
- Production ready with auth, rate limiting, and monitoring
- High performance using FastAPI with async support
- AI agnostic – integrate any AI provider easily
- Secure by default with token auth and input validation
- Batch processing for handling multiple queries efficiently

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name AI Customer Support Bot - MCP Server
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. Enable "Start Automatically" if you want the plugin to start when Highlight launches

From the repository

Clone the repository, create a Python virtual environment, install dependencies, copy .env.example to .env and configure your database URL, secret key, and rate limits. Create a PostgreSQL database, then run python app.py to start the server on http://localhost:8000. Interact via endpoints like POST /mcp/process (single query) or POST /mcp/batch (batch processing), using X-MCP-Auth for token authentication.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "ai customer support bot - mcp server": {
            "AI-Customer-Support-Bot---MCP-Server": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

McpServers

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

πŸ€– AI Customer Support Bot - MCP Server

<div align="center"> Python FastAPI PostgreSQL MCP License A modern, extensible MCP server framework for building AI-powered customer support systems Features β€’ Quick Start β€’ API Reference β€’ Architecture β€’ Contributing </div> ---

🌟 Overview

A Model Context Protocol (MCP) compliant server framework built with modern Python. Designed for developers who want to create intelligent customer support systems without vendor lock-in. Clean architecture, battle-tested patterns, and ready for any AI provider. ``mermaid graph TB Client[HTTP Client] --> API[API Server] API --> MW[Middleware Layer] MW --> SVC[Service Layer] SVC --> CTX[Context Manager] SVC --> AI[AI Integration] SVC --> DAL[Data Access Layer] DAL --> DB[(PostgreSQL)] `

✨ Features

<table> <tr> <td> πŸ—οΈ Clean Architecture Layered design with clear separation of concerns πŸ“‘ MCP Compliant Full Model Context Protocol implementation </td> <td> πŸ”’ Production Ready Auth, rate limiting, monitoring included πŸš€ High Performance Built on FastAPI with async support </td> </tr> <tr> <td> πŸ”Œ AI Agnostic Integrate any AI provider easily πŸ“Š Health Monitoring Comprehensive metrics and diagnostics </td> <td> πŸ›‘οΈ Secure by Default Token auth and input validation πŸ“¦ Batch Processing Handle multiple queries efficiently </td> </tr> </table>

πŸš€ Quick Start

Prerequisites

- Python 3.8+ - PostgreSQL - Your favorite AI service (OpenAI, Anthropic, etc.)

Installation

``bash
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