AI Customer Support Bot - MCP Server
@ChiragPatankar
About AI Customer Support Bot - MCP Server
No overview available yet
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"AI-Customer-Support-Bot--MCP-Server": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}Tools
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Overview
What is AI Customer Support Bot - MCP Server?
A Model Context Protocol (MCP) compliant server framework built with Python, FastAPI, and PostgreSQL. It lets developers create intelligent, AI-powered customer support systems without vendor lock-in. Clean architecture with layered design for production readiness.
How to use AI Customer Support Bot - MCP Server?
Clone the repository, create a virtual environment with Python 3.8+, install dependencies from requirements.txt, copy and edit .env.example to configure your database and AI service credentials, then run python app.py. The server starts at http://localhost:8000.
Key features of AI Customer Support Bot - MCP Server
- Full MCP protocol implementation
- Production-ready with auth, rate limiting, and monitoring
- High performance with FastAPI async support
- AI-agnostic – integrate any provider (OpenAI, Anthropic, etc.)
- Batch processing for multiple queries
- Secure by default: token auth, input validation, audit logging
Use cases of AI Customer Support Bot - MCP Server
- Automate customer support queries with AI-generated responses
- Process high volumes of support tickets via batch API
- Build a vendor-independent support bot that can switch AI providers
- Monitor and scale support system with built-in health metrics
FAQ from AI Customer Support Bot - MCP Server
What is MCP?
MCP stands for Model Context Protocol. This server implements the full MCP specification for interoperability with AI services.
What are the runtime requirements?
Python 3.8+, a PostgreSQL database, and credentials for an AI service (e.g., OpenAI, Anthropic).
How do I add my own AI provider?
Install the provider’s SDK, add its API key and model to your .env file, then implement a service class that generates responses from the AI model.
How is authentication handled?
The server uses token-based authentication passed via the X-MCP-Auth header on all API requests.
Does the server support scaling?
Yes. For production, use connection pooling, add Redis for distributed rate limiting, and deploy behind a load balancer. Docker support is coming soon.
Frequently asked questions
What is MCP?
MCP stands for Model Context Protocol. This server implements the full MCP specification for interoperability with AI services.
What are the runtime requirements?
Python 3.8+, a PostgreSQL database, and credentials for an AI service (e.g., OpenAI, Anthropic).
How do I add my own AI provider?
Install the provider’s SDK, add its API key and model to your `.env` file, then implement a service class that generates responses from the AI model.
How is authentication handled?
The server uses token-based authentication passed via the `X-MCP-Auth` header on all API requests.
Does the server support scaling?
Yes. For production, use connection pooling, add Redis for distributed rate limiting, and deploy behind a load balancer. Docker support is coming soon.
Basic information
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