Reexpress MCP Server provides a drop-in integration to add a statistically robust verification step to your LLM workflows. After starting the server, you append the Reexpress prompt to the end of your chat or tool-calling interaction. The MCP server will engage its pre-trained SDM estimator ensemble (including gpt-5.2-2025-12-11 and gemini-3-pro-preview) to compute a calibrated verification probability and present a robust uncertainty estimate based on calibrated training and OpenVerification1 data. If you want to refine future outputs, you can use the ReexpressAddTrue or ReexpressAddFalse tools after a verification completes; subsequent verifications will take these updates into account. The server also ships with training scripts so you can retrain or experiment with alternative underlying LLMs if needed. The system is designed to operate locally on Linux or macOS, using local model executions to protect data and minimize external API exposure.

Prerequisites:

• Node.js and npm (if choosing the Node-based deployment path) or Python, depending on your deployment preference (see INSTALL.md in the project for exact requirements).
• Access to a machine capable of running the required LLM models locally (e.g., granite-3.3-8b-instruct via HuggingFace transformers).

Installation steps (Node-based path):

1. Clone the repository or create your project folder: git clone https://github.com/your-org/reexpressai-reexpress_mcp_server.git cd reexpressai-reexpress_mcp_server

2. Install dependencies (example with npm): npm install

3. Configure environment variables (example):

   .env

   PORT=8080 LOG_LEVEL=info

   Add any model or API configuration as required by your deployment

4. Start the MCP server: npm run start

(If you prefer a non-NPM path, adapt to your chosen deployment method and consult INSTALL.md for Python/uvx guidance or Docker deployment instructions.)

Tips and caveats:

• Ensure the machine can locally load the required models (e.g., granite-3.3-8b-instruct) via HuggingFace transformers; ensure sufficient RAM/VRAM for your chosen model.
• Review CONFIG.md and accompanying documentation (HOW_TO_USE.md, OUTPUT_HTML.md, etc.) for specifics on configuration options and result formats.
• When running in production, consider securing the server behind proper authentication and restricting API access to trusted clients.
• If you encounter data-handling concerns, remember that data handling adheres to local processing for SDM estimation; verify your file-access permissions and the use of ReexpressDirectorySet/ReexpressFileSet as described in the docs.
• The npm_package field notes the package name if you publish this as an npm module; adjust the deployment method accordingly if you publish or install via npm.