Overview

Reexpress is a Model‑Context‑Protocol (MCP) server that injects statistically robust verification into LLM pipelines. By acting as a second‑opinion service, it provides developers with a principled confidence estimate for every assistant response. This is especially valuable in software‑engineering and data‑science workflows where correctness, reproducibility, and auditability are paramount. The server uses a pre‑trained Similarity‑Distance‑Magnitude (SDM) estimator that ensembles multiple high‑performing LLMs—GPT‑5, Gemini 2.5‑Pro, and a locally hosted Granite model—to compare the assistant’s output against a curated database of training examples from the OpenVerification1 dataset. The result is a calibrated probability that the answer is correct, enabling downstream decision‑making.

Why it matters

Traditional LLMs output a single best guess without any indication of uncertainty, which can lead to costly mistakes in mission‑critical applications. Reexpress bridges this gap by turning the LLM into a verified agent: after every answer, the verifier evaluates the response, and the LLM can use that feedback to refine its next step. This “reasoning with SDM verification” allows agents to decide when they need additional tools, external data, or user clarification—effectively giving them a safety net that is both transparent and auditable.

Key capabilities

• Dynamic confidence scoring – The verifier produces a probability that the answer is correct, updated in real time as new evidence (e.g., user‑provided “true/false” annotations) is added.
• Ensemble inference – By combining outputs from multiple LLMs, the estimator achieves higher calibration and robustness than any single model.
• Local processing – All heavy computation occurs on the client machine; only minimal text is sent to external APIs, preserving privacy and reducing latency.
• File‑access control – Developers can explicitly grant or restrict which local files are sent to the LLMs via and , keeping sensitive data out of the cloud.
• Retraining hooks – The repository includes training scripts, enabling teams to fine‑tune the verifier on domain‑specific data or swap in alternative underlying LLMs.

Real‑world use cases

• Code review assistants – Verify that generated code follows style guidelines and meets functional requirements before merging.
• Data‑analysis pipelines – Confirm statistical claims or model predictions, flagging uncertain results for human review.
• Knowledge‑base QA – Ensure that answers retrieved from internal documentation are accurate, reducing misinformation.
• Regulatory compliance – Provide an audit trail of confidence scores for decisions made by AI agents in regulated industries.

Integration with MCP workflows

Reexpress is designed as a drop‑in MCP server. After installing, developers simply append the prompt to their chat history or invoke it as a tool. The LLM (e.g., Claude Opus 4.1) sends its response to the server, receives a calibrated probability, and can then decide whether to accept the answer, request additional data, or ask for clarification. Because it adheres strictly to MCP standards, Reexpress works seamlessly with any MCP‑compliant client on Linux or macOS, and can be paired with web‑search servers or domain‑specific retrieval engines for richer contexts.

Reexpress transforms the way developers build reliable AI agents, turning opaque language models into accountable systems that quantify uncertainty and self‑correct when needed.