Wegene Assistant MCP Server

Overview

The WeGene Assistant MCP server bridges the gap between AI assistants and personal genetic data. By exposing a user’s WeGene reports as structured resources, it lets an LLM read and interpret genetic test results directly within a conversational context. This removes the need for developers to build custom parsers or handle raw API responses, enabling rapid creation of health‑oriented AI experiences.

Once a user authorizes the server via WeGene’s OAuth flow, every report in their account becomes an accessible resource through a custom URI scheme. Each resource carries metadata—name, description, and a JSON MIME type—so the LLM can discover and retrieve reports with minimal effort. This design follows MCP’s resource abstraction, keeping data access uniform across tools.

The server supplies a small but powerful toolset. initiates the browser‑based OAuth flow, ensuring the LLM has permission to read reports. lists all user profiles, while returns a catalog of available reports with endpoints and descriptions. Finally, fetches the full JSON payload for a specific report, given its endpoint, ID, and profile. These tools let an assistant ask questions like “Show me my cholesterol levels” or “Explain the significance of my genetic predisposition to hypertension,” and receive precise, machine‑readable answers.

For developers, the server’s value lies in its plug‑and‑play integration. A single MCP configuration line adds the WeGene assistant to Claude Desktop, and the LLM can invoke tools or read resources without custom API wrappers. This streamlines workflows for health‑tech startups, research labs, or personal wellness apps that want to provide AI‑driven insights into genetic data.

Typical use cases include:
Personalized health coaching—an assistant can recommend lifestyle changes based on genetic risk factors.
Clinical decision support—clinicians can query a patient’s report history during consultations.
Research data aggregation—studies can collect anonymized genetic insights from consenting participants via an AI interface.

Because the server exposes data as first‑class resources and offers a clear, authenticated tool chain, it stands out as a ready solution for any developer looking to integrate sensitive genetic information into conversational AI without compromising security or compliance.