openjev-mcp
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- AlicenseNot gradedqualityBmaintenanceEnables structured, rubric-based evaluation of text or structured state using TypeSafe AI's Jev System One API, supporting yes/no, single-choice, and rubric-scored questions in parallel.Apache 2.0
- 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.51AGPL 3.0
- 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.5MIT
- AlicenseNot gradedqualityBmaintenanceEnables MCP clients to consult TypeSafe's Jev through a judge tool, answering narrow typed questions with calibrated probabilities instead of prose.MIT
- AlicenseNot gradedqualityAmaintenanceEnables Claude Code or any MCP client to ask TypeSafe's Jev for calibrated, typed judgments (probabilities, choices, scores) instead of prose, with local caching and cost tracking.MIT
- AlicenseAqualityAmaintenanceEnables prototyping, running, and evaluating typed judgment questions against TypeSafe's Jev model, including accuracy, calibration, and threshold analysis.31MIT
TDQS
Scored across 4 tools
Each single-question tool has a clearly distinct response type (choice, score, noul), and jev_ask is explicitly the batch variant. There is minor overlap because jev_ask can also ask choice, score, or noul questions, but the batch-vs-single distinction is clear.
All tools share the consistent 'jev_' prefix, making them instantly recognizable. The suffix mixes a verb ('ask') with response-type nouns ('choice', 'score', 'noul'), but the pattern is still predictable and readable.
Four tools is a well-scoped set for a specialized question-answering server: one batch entry point and three focused question types. Each tool earns its place without redundancy or bloat.
The tool surface covers the core domain of typed judgments well: batch asking plus choice, score, and yes/no variants. Minor gaps such as conversation history or model configuration exist, but they are not essential to the stated purpose.