Jev AI MCP
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Related Servers
- AlicenseAqualityBmaintenanceEnables AI coding agents to offload yes/no, multiple-choice, and scoring questions to TypeSafe's Jev, returning compact confidence-scored answers to save tokens and improve speed.1MIT
- AlicenseBqualityCmaintenanceEnables AI agents to obtain typed judgments from TypeSafe's Jev System One models, including yes/no probabilities, multiple-choice selections with distributions, and rubric-based scores, directly usable in code.52AGPL 3.0
- AlicenseBqualityBmaintenanceEnables coding agents to obtain probabilistic decisions from Jev AI via OpenRouter for classification, scoring, and validation, with tools like jev_check, jev_classify, jev_score, and jev_evaluate.53MIT
- AlicenseAqualityCmaintenanceEnables coding or reasoning agents to request structured judgments from TypeSafe's Jev model at decision points, including choices, scores, claim verification, and code reviews, with probabilities and confidence returned as data.59 npmMIT
- AlicenseBqualityCmaintenanceEnables AI agent skills to route state-evaluation requests to TypeSafe AI's Jev System One model, supporting typed questions, choice classification, binary probabilities, and rubric scoring with calibrated confidence.42MIT
- AlicenseNot gradedqualityBmaintenanceConnects AI coding agents to the Jev decision model, enabling fast and cheap structured decisions such as task classification, strategy selection, complexity rating, and escalation decisions.MIT
TDQS
Scored across 6 tools
Each tool has a distinct purpose: classification, scoring, yes/no checking, gating, multi-question decisions, and saved judge evaluation. The descriptions cross-reference each other (e.g., 'Use jev_score for ordered levels'), which helps disambiguate, though jev_gate and jev_check could be confused in some safety contexts.
All tools follow a consistent 'jev_' prefix with a clear verb or noun indicating the operation: classify, score, check, gate, decide, saved_judge. The naming pattern is uniform and predictable.
Six tools is well-scoped for a decision/evaluation API. Each tool covers a distinct mode of interaction (single vs. batch, inline vs. saved judge), and none feel redundant or excessive.
The set covers the core decision types: classification, scoring, yes/no, gating, batch decisions, and saved judges. A minor gap is the lack of a tool to create/manage saved judges, but the server's stated purpose is evaluation, not judge management.