Reexpress Model-Context-Protocol (MCP) Server

What is Reexpress MCP Server?

Reexpress MCP Server is a drop-in solution that adds state-of-the-art statistical verification to LLM pipelines and everyday LLM use for software development and data science. It integrates with tool-calling LLMs (e.g., Claude Opus 4.7) and MCP clients on macOS (Tahoe 26 or later on Apple silicon) or Linux, using a pre-trained Similarity-Distance-Magnitude (SDM) estimator to provide a robust estimate of predictive uncertainty.

How to use Reexpress MCP Server?

Install the MCP server, then add the Reexpress prompt to the end of your chat text. The tool-calling LLM checks its response with the provided pre-trained SDM estimator, which ensembles gpt-5.5-2026-04-23, gemini-3.1-pro-preview, and gemini-embedding-2. After a verification completes, you can adapt the model to your tasks by calling the ReexpressAddTrue or ReexpressAddFalse tools.

Key features of Reexpress MCP Server

• First reliable, statistically robust AI second opinion for AI workflows
• Computes predictive uncertainty using an SDM estimator locally
• Dynamically updatable via ReexpressAddTrue and ReexpressAddFalse tools
• Simple, conservative file access system
• Runs on macOS and Linux with minimal compute requirements
• Enables reasoning with verification for tool-calling LLMs

Use cases of Reexpress MCP Server

• Adding statistical verification to complex LLM pipelines
• Improving search and QA workflows in software development and data science
• Enabling LLMs to progressively refine answers using uncertainty estimates
• Determining when a model needs additional resources or user clarification

FAQ from Reexpress MCP Server

What is the Reexpress MCP Server?

It is a drop-in solution for adding statistically robust confidence estimates to LLM outputs, using an SDM estimator that ensembles multiple generative models and an embedding model locally on your computer.

Does it call external APIs?

Yes. Data is sent via standard LLM API calls to Azure/OpenAI and Google for the generative models; the SDM estimator processing is done entirely locally on your machine.

What are the system requirements?

The server runs on Linux and macOS. The primary requirement is the ability to run a small 3 million parameter PyTorch model locally, so compute needs are minimal.

Who is the target audience?

Developers and data scientists familiar with LLMs, MCP, and command-line tools.

How does the file access system work?

You control which additional files get sent to LLM APIs by explicitly specifying files via the ReexpressDirectorySet() and ReexpressFileSet() tools.