Chiragpatankar AI Customer Support Bot MCP Server

🚀 AI Customer Support Bot - MCP Server

An AI-powered customer support service Model Context Protocol (MCP) server integrated with Cursor AI and Glama.ai.

✨ Features

• Retrieve context from Glama.ai in real-time.
• Generate automatic responses based on Cursor AI.
• Support batch processing.
• Implement small file queues.
• Provide rate-limiting functionality.
• Track user interactions.
• Conduct health monitoring.
• Comply with the MCP protocol.

📦 Installation

Prerequisites

• Python 3.8+
• PostgreSQL database
• Glama.ai API Key
• Cursor AI API Key

Steps

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  # 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 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

# Other configuration options
DEBUG=False
LOG_LEVEL=INFO

6. Installation is complete! Proceed to the next step to start the server.

🚀 Quick Start

Activate the virtual environment (if already created):

source venv/bin/activate  # Unix/MacOS
# Or
venv\Scripts\activate  # Windows

Start the server:

python app.py

📚 Documentation

API Endpoints

Health Check

• GET /health
• Returns service status information.

Context Processing

• POST /process_context
• Processes context data for customer support requests.
• Supported format: JSON

Batch Processing

• POST /batch_process
• Processes multiple customer support requests.
• Supported format: JSON array

Rate Limiting

• All MCP endpoints require authentication via the X-MCP-Auth header.
• Implement rate-limiting to prevent user abuse.
• Keep database credentials secure.
• Avoid committing API keys to version control.

Monitoring

The server provides the following monitoring endpoints:

• Service status
• Rate-limiting usage
• Connected service status
• Processing time

Development

1. Update mcp_config.py to add new configuration options.
2. Add new models in models.py if needed.
3. Create new endpoints in app.py.
4. Update functional endpoints to reflect new features.

Security

• All MCP endpoints require authentication via the X-MCP-Auth header.
• Implement rate-limiting to prevent user abuse.
• Keep database credentials secure.
• Avoid committing API keys to version control.

Support

If you need support, please create an issue in the repository or contact the development team.