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.
@ReexpressAI
基本信息
Reexpress Model-Context-Protocol (MCP) Server
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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.
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.
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.
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.
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.
Developers and data scientists familiar with LLMs, MCP, and command-line tools.
You control which additional files get sent to LLM APIs by explicitly specifying files via the ReexpressDirectorySet() and ReexpressFileSet() tools.

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