# jev-mcp jev-mcp is an open-source [[Model Context Protocol (MCP)|MCP]] server by jkudish that turns [[Jev]] into twelve judgment tools any MCP client can call ([[Claude Code]], [[OpenAI Codex|Codex]], [[OpenCode]], Amp...). Each call returns typed probabilities (and usually a confidence score) in roughly 150 to 500 ms, for a fraction of a cent. The pitch is about the checks agents skip. A frontier model is too slow and too expensive to verify every claim, page or candidate list, so agents just... don't. At Jev prices, you can afford to check everything. The tools: - **`jev_verify`**: checks each claim in a report or PR description against its sources (verified, contradicted, unsupported) - **`jev_screen`**: judges fetched text BEFORE it enters context: probability of [[Prompt injection|prompt injection]], substance, relevance. It recommends pass, review, block or skip, but never blocks on its own - **`jev_noul`**: calibrated probability for up to 64 propositions per call - **`jev_find`** and **`jev_rerank`**: rank candidates against a plain-language query, with no embeddings and no index to maintain - **`jev_classify`**, **`jev_decide`** and **`jev_compare`**: batch labelling, choosing between alternatives (with an "ask the user" escape hatch), and judging how two passages relate - **`jev_extract`** and **`jev_audit`**: pull values (prices, dates, IDs) with regex plus judgment, then audit them against the source ([[Candidate-Then-Select Extraction]]) - **`jev_review`** and **`jev_gate`**: score a diff for correctness, spec match, test gaps and blast radius, and check "tests pass" claims against the evidence before a task is called done What I like is the defensive design. Malformed answers "fail closed": the claim gets `unknown` and is flagged for review instead of being silently accepted. Thresholds are explicit parameters (`auto_accept` defaults to 0.8, `block_at` to 0.75). And `jev_verify` follows TypeSafe's own [[Atomic Question Decomposition]] advice: it asks a separate "is this evidence even about the same thing?" question, and a contradiction only stands if the answer is yes. That's one more question in the same request, not another API call. It runs over stdio by default, or as a stateless HTTP server for a team (with a required bearer token, since every call spends your Jev key). It also ships an [[AI Agent Skills|agent skill]] that teaches coding agents WHEN to reach for each tool, which addresses a real problem: MCP tools that sit registered but never get called. The author calls it early software, and the accuracy figures in the repo come from contributors' private experiments, not independent benchmarks. Treat the thresholds as starting points and tune them on your own cases ([[Confidence-Gated Routing]]). ## References - [jev-mcp on GitHub](https://github.com/jkudish/jev-mcp) - [@jkudish/jev-mcp on npm](https://www.npmjs.com/package/@jkudish/jev-mcp) ## Related - [[Jev]] - [[Model Context Protocol (MCP)]] - [[System One Models]] - [[Confidence-Gated Routing]] - [[Atomic Question Decomposition]] - [[Candidate-Then-Select Extraction]] - [[Prompt injection]] - [[AI Guardrails]]