chandan-akshronix/insurance-ai-mcp-server
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Main Control Point (MCP) backend service for the AI Automated Insurance Claim & Underwriting System.
Gk Mcp
MCP Server built using Python
MCP Protocol Compliance: See for details.
Authentication
See for details.
MCP Server Components
Tools
Tools are functions that AI models can call to perform specific actions.
Prompts
Prompts are reusable templates that help AI models interact consistently with your server.
Resources
Resources provide AI models with access to external content.
Security Features
- Implements origin validation to prevent DNS rebinding attacks
- Uses Streamable HTTP transport in stateless mode for security
- Only processes requests from trusted origins (localhost) in development
- All API inputs are validated before processing
Project Layout
This project is organized using the src layout structure.
The following is a list of the src/insurance_mcp subdirectories and files:
| Folder | Description |
|---|---|
| /config | MCP Server configuration files for MCP Server deployment in local and cloud environments. |
| /controllers | The file mcp_controller.py exposes the MCP Tools provided by this MCP Server and invokes the business logic services. |
| /services | Business logic services. |
| /repositories | Data access/repository layer. |
| /middleware | Filters and Handlers classes to support MCP Message Handling best practices |
| /utils | Utility classes and exceptions used across MCP tools during request execution. |
| /models | Defines the data models. |
| server.py | HTTP server with MCP streamable capabilities |
Run the MCP Server
Prerequisites
Step 1: Install uv Package Manager
- Install the package using uv: A fast Python Package Manager (You don't need to be connected to a VPN to download it)
Step 2: Clone Repository
- Clone the repository:
git clone repo_link
cd repo_name
Step 3: Configuration
PORT: Server port (default: 8080)
Step 4: Set Up Python Environment
- Install the dependencies with [uv]
uv sync
source .venv/bin/activate
Step 5: Run the Unit Tests
Run the test suite:
uv pip install "insurance-mcp[tests]"
uv run pytest
Step 6: Start the MCP Server
- Configure authentication using the steps above
- Start the MCP Server:
insurance-mcp
Congratulations! Your MCP server is now running at https://localhost:8080/mcp/
Step 7: Test the MCP Server
Initial setup includes the following MCP Server Tools:
get_by_id: Allows LLMs to get an gkmcp by IDmcp_list: Enables LLMs to get all gkmcps
📝 Detailed Testing: See
Deployment
Health Check Validation
https://deployed_link.com/health
Monitoring & Observability
Golden Signals Dashboard
The server exposes Prometheus metrics for Monitoring and Observability.
For metrics and Golden Signals configurations check this project's
Distributed Tracing
This MCP server supports the OpenTelemetry distributed tracing standard.
Tracestore Onboarding
Development
Contributions are welcome! Please follow the guidelines below:
- Fork the repository.
- Create a feature branch.
- Commit your changes.
- Submit a pull request.
Support
For issues or questions:
insurance-ai-mcp-server
Main Control Point (MCP) backend service for the AI Automated Insurance Claim & Underwriting System. Handles claim orchestration, Kafka-based messaging, and integration with AI agents, databases, and observability tools.