Wegene-Assistant MCP Server: Empowering AI Applications with Genetic Data Analysis

Overview: What is Wegene-Assistant MCP Server?

The Wegene-Assistant MCP server is designed to facilitate the integration of genetic data analysis into various AI applications using the Model Context Protocol (MCP). This server connects to WeGene, a leading genomics service provider, enabling AI models to access and interpret users' genetic testing reports. By implementing tools like OAuth for authorization, report retrieval, and metadata extraction, this server ensures secure and efficient data handling.

🔧 Core Features & MCP Capabilities

The core features of the Wegene-Assistant MCP server include a robust set of tools designed to interact with WeGene's Open API platform. These tools are powered by MCP, ensuring seamless communication between the AI application and the genetic testing reports stored on WeGene servers. The key resources exposed by this server—custom URI schemes for accessing reports and JSON data representations—are essential for building integrations that require dynamic access to user-specific data.

Tools Overview

1. wegene-oauth: This tool initiates an OAuth process, allowing users to authorize the MCP server to access their WeGene genetic testing reports. Users must complete this authorization within 120 seconds to enable subsequent report retrieval by the AI application.

2. wegene-get-profiles: Retrieves a list of user profiles from WeGene along with their associated IDs. This data is crucial for the AI model to identify and process relevant reports.

3. wegene-get-report-info: Provides metadata about available genetic reports, including names, descriptions, and endpoints. These details help in preparing the AI application to handle specific types of genetic analysis requests.

4. wegene-get-report: Fetches detailed results from a specified report within a user's profile. This tool accepts parameters for report_endpoint, report_id, and profile_id to access the exact data required by the AI model.

⚙️ MCP Architecture & Protocol Implementation

The Wegene-Assistant MCP server leverages the Model Context Protocol (MCP) to establish a standardized framework for communication between the AI application and various external tools and data sources. The server is structured as follows:

1. Authorization Layer: Implements OAuth protocols to secure user authentication.
2. Data Retrieval Layer: Configures methods to fetch genetic report data from WeGene's servers.
3. Metadata Layer: Manages the extraction and storage of metadata related to available reports.

MCP ensures that communication among these layers is consistent, enabling any compliant AI application to integrate seamlessly with Wegene-Assistant server and subsequent tools or data sources.

Mermaid Diagram: MCP Protocol Flow

graph TD
    A[AI Application] -->|MCP Client| B[MCP Server]
    B --> C[WeGene API]
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#e8f5e8

Mermaid Diagram: Data Architecture

graph LR
    subgraph AI Application
        A[[MCP Client]] --> B[Data Source/Tool]
        B --> C[MCP Server]
        C -->|JSON Responses| A
    end
    subgraph WeGene API
        D[Custom URI Schemes] --> E[Report Data & Metadata]
        F[OAuth Authn] --> G[Profile Access Control]
        H[API Endpoints] --> I[Genetic Report Results]
        D -- Authentication --> G
        G -- Authorization --> F, H, I
    end

🚀 Getting Started with Installation

To get started with the Wegene-Assistant MCP server, follow these steps:

Install via Smithery

npx -y @smithery/cli install @xraywu/mcp-wegene-assistant --client claude

Local Installation

1. Clone this project.
2. Run uv sync --dev --all-extras under the project's root folder.

Ensure you have a valid WeGene Open API key/secret to proceed with configuration.

💡 Key Use Cases in AI Workflows

The Wegene-Assistant MCP server can be integrated into various AI workflows, enhancing usability and data accessibility. Here are two practical use cases:

Real-Time Genetic Report Analysis

This scenario involves an AI application that receives a user's genetic report and instantly provides insights. The integration requires the following steps:

1. User authorization triggered via wegene-oauth.
2. WeGene API calls processed using wegene-get-report to fetch necessary information.
3. Data analysis performed within the AI model.

{
  "mcpServers": {
    "wegene-assistant": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/wegene-assistant",
        "run",
        "wegene-assistant"
      ]
    }
  }
}

Custom Report Summarization

Another use case is generating custom summaries based on genetic test results. In this workflow, the AI application first retrieves profiles and report metadata using wegene-get-report-info before analyzing specific reports to compile a summary.

🔌 Integration with MCP Clients

The Wegene-Assistant MCP server supports multiple MCP clients that can leverage its functionalities:

• Claude Desktop: Fully supported for all resources and tools.
• Continue: Supported for resource access but lacks direct prompt support.
• Cursor: Primarily supports tool integration without full client compatibility.

📊 Performance & Compatibility Matrix

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

🛠️ Advanced Configuration & Security

For advanced users, configuring the Wegene-Assistant server involves setting up environment variables and running the server with specific commands:

cp .env.example .env

Edit .env to include your WeGene API credentials, then run setup and start scripts:

uv sync --dev --all-extras
uv run wegene-assistant

Security measures should be in place for handling sensitive data such as OAuth tokens.

❓ Frequently Asked Questions (FAQ)

Q1: How do I install the Wegene-Assistant MCP server?

Follow the installation instructions provided in the README, including using Smithery or running uv sync locally.

Q2: What tools are included with this server?

Tools include wegene-oauth for OAuth initiation, wegene-get-profiles to retrieve user profiles, and more.

Q3: Can I use this MCP server with any AI models supported by Claude Desktop?

Yes, the server supports seamless integration using the MCP protocol as used by Claude Desktop and other compatible clients.

Q4: How does the security setup look for accessing WeGene reports?

Security is handled via OAuth and API key/secret stored in environment variables to ensure only authorized access to user data.

Q5: Where can I find support or documentation if I encounter issues?

Check the project's GitHub repository for additional resources, FAQs, and community support forums.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! To contribute, fork this repository and send a pull request. For detailed guidelines on development and bug reporting, refer to the Contributing Guide section in the README.

🌐 MCP Ecosystem & Resources

Explore more about the MCP ecosystem at Model Context Protocol. For additional resources and community support, visit relevant forums and documentation sites.

By implementing the Wegene-Assistant MCP server, developers can significantly enhance their AI workflows by integrating genetic data analysis capabilities into various applications, ensuring reliable and secure data handling.