What is Ai Customer Support Bot Mcp Server

The AI Customer Support Bot is an MCP server that leverages Cursor AI and Glama.ai integration to provide intelligent and real-time customer support. It processes queries and fetches contextual information to generate accurate responses, aiming to enhance overall customer service experiences.

Use cases

This bot can be employed in various customer support scenarios such as handling common inquiries, password resets, business hours information, and general support questions. It is ideal for businesses looking to automate their customer interaction while maintaining high-quality support.

How to use

To use the AI Customer Support Bot, start by setting up the server with your credentials, including the necessary API keys and database configurations. After running the server, you can interact with it through provided API endpoints to process single or batch queries and check server health.

Key features

Key features of the AI Customer Support Bot include real-time context fetching from Glama.ai, AI-based response generation with Cursor AI, support for batch processing, priority queuing, rate limiting, user interaction tracking, and health monitoring. It also complies with MCP protocols for standardized communication.

Where to use

The AI Customer Support Bot can be implemented in diverse environments where customer interaction is vital, such as e-commerce websites, service-oriented businesses, and help desks. It is suitable for sectors that require efficient query handling and quick response times, like tech support, customer service, and online retail.

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 <repository-url>
cd <repository-name>

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.