AI Customer Support Bot - MCP Server Development by Chirag PatankarAI Customer Support Bot - MCP Server Development by Chirag Patankar

AI Customer Support Bot - MCP Server Development

Chirag Patankar

Completed work

AI Engineer

Cursor

PostgreSQL

Python

Electronics

AI Customer Support Bot - MCP Server

A Model Context Protocol (MCP) server that provides AI-powered customer support using Cursor AI and Glama.ai integration.

Features

Real-time context fetching from Glama.ai

AI-powered response generation with Cursor AI

Batch processing support

Priority queuing

Rate limiting

User interaction tracking

Health monitoring

MCP protocol compliance

Prerequisites

Python 3.8+

PostgreSQL database

Glama.ai API key

Cursor AI API key

Installation

Clone the repository:

git clone <repository-url>
cd <repository-name>

Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Create a .env file based on .env.example:

cp .env.example .env

Configure your .env file with your credentials:

# API Keys
GLAMA_API_KEY=your_glama_api_key_here
CURSOR_API_KEY=your_cursor_api_key_here

# Database
DATABASE_URL=postgresql://user:password@localhost/customer_support_bot

# API URLs
GLAMA_API_URL=https://api.glama.ai/v1

# Security
SECRET_KEY=your_secret_key_here

# MCP Server Configuration
SERVER_NAME="AI Customer Support Bot"
SERVER_VERSION="1.0.0"
API_PREFIX="/mcp"
MAX_CONTEXT_RESULTS=5

# Rate Limiting
RATE_LIMIT_REQUESTS=100
RATE_LIMIT_PERIOD=60

# Logging
LOG_LEVEL=INFO

Set up the database:

# Create the database
createdb customer_support_bot

# Run migrations (if using Alembic)
alembic upgrade head

Running the Server

Start the server:

python app.py

The server will be available at http://localhost:8000

API Endpoints

1. Root Endpoint

GET /

Returns basic server information.

2. MCP Version

GET /mcp/version

Returns supported MCP protocol versions.

3. Capabilities

GET /mcp/capabilities

Returns server capabilities and supported features.

4. Process Request

POST /mcp/process

Process a single query with context.

Example request:

curl -X POST http://localhost:8000/mcp/process \
  -H "Content-Type: application/json" \
  -H "X-MCP-Auth: your-auth-token" \
  -H "X-MCP-Version: 1.0" \
  -d '{
    "query": "How do I reset my password?",
    "priority": "high",
    "mcp_version": "1.0"
  }'

5. Batch Processing

POST /mcp/batch

Process multiple queries in a single request.

Example request:

curl -X POST http://localhost:8000/mcp/batch \
  -H "Content-Type: application/json" \
  -H "X-MCP-Auth: your-auth-token" \
  -H "X-MCP-Version: 1.0" \
  -d '{
    "queries": [
      "How do I reset my password?",
      "What are your business hours?",
      "How do I contact support?"
    ],
    "mcp_version": "1.0"
  }'

6. Health Check

GET /mcp/health

Check server health and service status.

Rate Limiting

The server implements rate limiting with the following defaults:

100 requests per 60 seconds

Rate limit information is included in the health check endpoint

Rate limit exceeded responses include reset time

Error Handling

The server returns structured error responses in the following format:

{
  "code": "ERROR_CODE",
  "message": "Error description",
  "details": {
    "timestamp": "2024-02-14T12:00:00Z",
    "additional_info": "value"
  }
}

Common error codes:

RATE_LIMIT_EXCEEDED: Rate limit exceeded

UNSUPPORTED_MCP_VERSION: Unsupported MCP version

PROCESSING_ERROR: Error processing request

CONTEXT_FETCH_ERROR: Error fetching context from Glama.ai

BATCH_PROCESSING_ERROR: Error processing batch request

Development

Project Structure

.
├── app.py              # Main application file
├── database.py         # Database configuration
├── middleware.py       # Middleware (rate limiting, validation)
├── models.py          # Database models
├── mcp_config.py      # MCP-specific configuration
├── requirements.txt   # Python dependencies
└── .env              # Environment variables

Adding New Features

Update mcp_config.py with new configuration options

Add new models in models.py if needed

Create new endpoints in app.py

Update capabilities endpoint to reflect new features

Security

All MCP endpoints require authentication via X-MCP-Auth header

Rate limiting is implemented to prevent abuse

Database credentials should be kept secure

API keys should never be committed to version control

Monitoring

The server provides health check endpoints for monitoring:

Service status

Rate limit usage

Connected services

Processing times

Contributing

Fork the repository

Create a feature branch

Commit your changes

Push to the branch

Create a Pull Request

Flowchart

Verification Badge

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support, please create an issue in the repository or contact the development team.

Like this project

Completed work

Posted May 27, 2025

Developed an MCP server for AI-powered customer support using Cursor AI and Glama.ai.

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