openjev-mcp
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- AlicenseAqualityCmaintenanceEnables agents to query the TypeSafe Jev decision model for typed answers—yes/no, choice, and score questions—with calibrated probabilities, batched in a single request under a local context-budget guard.6MIT
- 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
- AlicenseAqualityAmaintenanceEnables agents to ask TypeSafe AI's Jev decision model typed questions — choices, scores, true/false propositions, and image bounding boxes — about a text or JSON state, returning structured answers with calibrated probabilities and confidence instead of free-form prose. Supports images, multi-question batch calls, and long-context requests over stdio.6129 npmMIT
- 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
- 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
- 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.515 npmMIT
TDQS
Scored across 5 tools
Tool purposes are largely distinct: jev_choice, jev_score, and jev_noul target specific judgment types, while jev_batch handles multiple items. However, jev_ask can also handle multiple questions of various types, creating some overlap with the single-type tools, though the descriptions guide usage.
All tools use the 'jev_' prefix followed by a type or action (ask, choice, score, noul, batch) in consistent snake_case. The pattern is predictable and easy to parse.
Five tools is an appropriate number for this domain, covering general, specific, and batch judgment queries without redundancy or excessive fragmentation.
The surface covers single and multi-question requests, all three judgment types (choice, score, noul), and batch processing. No obvious gaps in the core functionality.