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

1. Clone the repository:

git clone 
cd 

2. Create and activate a virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

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

cp .env.example .env

5. 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

6. 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

1. Update mcp_config.py with new configuration options
2. Add new models in models.py if needed
3. Create new endpoints in app.py
4. 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

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. 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.