Skip to main content
Glama
deenesjakoruzh

AI Customer Support Bot - MCP Server

by ChiragPatankar
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote
MIT License
3

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

Related MCP server: MCP Starter

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>

  1. Create and activate a virtual environment:

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

  1. Install dependencies:

pip install -r requirements.txt

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

cp .env.example .env

  1. 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

  1. 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

. ā”œā”€ā”€ app.py # Main application file ā”œā”€ā”€ database.py # Database configuration ā”œā”€ā”€ middleware.py # Middleware (rate limiting, validation) ā”œā”€ā”€ models.py # Database models ā”œā”€ā”€ mcp_config.py # MCP-specific configuration ā”œā”€ā”€ requirements.txt # Python dependencies ā””ā”€ā”€ .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

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.

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

Resources

Appeared in Searches

New MCP Servers

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ChiragPatankar/AI-Customer-Support-Bot---MCP-Server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server