Insurance MCP Demo

A demonstration project that showcases how to build an AI-powered insurance assistant using the Model Context Protocol (MCP). The client uses an LLM (Qwen running locally through Ollama) to determine which MCP tools to call, executes those tools, and then generates a natural language response.

Features

• 🚀 MCP server built with FastMCP

• 💻 MCP client using stdio transport

• 🔍 Dynamic tool discovery (list_tools)

• 🛠️ Automatic tool selection using Qwen

• 🔄 MCP tool execution with call_tool

• 🤖 Natural language response generation

• 📦 Local LLM integration with Ollama

• 🏦 Insurance domain demo with customer, policy, premium, claim, fraud detection, and policy renewal tools

Related MCP server: Zava Insurance MCP Server

Project Structure

.
├── client.py          # MCP client and AI orchestration
├── server.py          # MCP server exposing insurance tools
├── requirements.txt
├── .env.example
└── README.md

Available MCP Tools

Requirements

• Python 3.10+

• Ollama

• Qwen 2.5 model

Installation

Clone the repository:

git clone https://github.com/<your-username>/insurance-mcp-demo.git
cd insurance-mcp-demo

Install dependencies:

pip install -r requirements.txt

Install the Qwen model:

ollama pull qwen2.5:3b

Start Ollama:

ollama serve

Running the Project

python client.py

The client will:

1. Launch the MCP server.

2. Perform the MCP handshake.

3. Discover available tools.

4. Ask the LLM which tools should be used.

5. Execute the selected MCP tools.

6. Generate a user-friendly response.

Example Queries

What is the status of claim CL001?

Is there any fraud risk on claim CL003?

Show me the details for policy P1002.

Please renew policy P1003.

Tell me about customer C002 and their policy.

Run a fraud check on claim CL002 and show me the claim status.

Technologies Used

• Python

• FastMCP

• Model Context Protocol (MCP)

• Ollama

• Qwen 2.5

• httpx

• Rich

Future Improvements

• Support multiple LLM providers

• Add conversation memory

• Integrate a real insurance database

• Add authentication and authorization

• Containerize with Docker

• Build a web interface using Streamlit or FastAPI

• Add unit and integration tests

License

This project is intended for educational and demonstration purposes.