jev-in-codex
Related Servers
Alternatives to jev-in-codex
No user-submitted related servers found.
Related Servers
- AlicenseAqualityBmaintenanceEnables coding agents to rank repository files, functions, and web search results using Jev-based probabilistic scoring, returning locations and confidence scores for the agent to review.3MIT
- AlicenseAqualityBmaintenanceEnables coding agents to query files, logs, and search results through TypeSafe Jev's typed, probabilistic answers, so they retrieve only the needed conclusion instead of raw context. Supports classification, scoring, yes/no checks, extraction, page ranking, and injection screening.11470 npmMIT
- AlicenseAqualityCmaintenanceEnables ranking large workspace search results and classifying English text into caller-defined labels using Jev via OpenRouter, with local secret filtering and spend guards.2MIT
- AlicenseAqualityBmaintenanceEnables coding agents to perform web research by using TypeSafe JEV to semantically filter search results and parsed content blocks, returning compact evidence with provenance instead of dumping raw pages into the downstream model context.13Apache 2.0
- AlicenseAqualityBmaintenanceEnables frontier coding agents to delegate routine probabilistic judgments to TypeSafe Jev, providing calibrated triage signals for failures, attempts, completion, context ranking, findings, risk, and generic evidence-grounded questions.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables agents to run hybrid retrieval, reciprocal rank fusion, knowledge graph expansion, semantic caching, and context reordering so critical evidence is placed at prompt boundaries to reduce lost-in-the-middle degradation.7MIT
TDQS
Scored across 1 tool
There is only one tool, so there is no possibility of confusing it with another tool. The purpose is singular and well-contained.
With a single tool named jev_label, there are no conflicting naming conventions. The name clearly follows the server's prefix and indicates its labeling purpose.
One tool feels thin for a labeling workflow, but it is not an extreme mismatch since the tool covers the core labeling operation end-to-end. Supporting operations like managing label definitions or reviewing outputs would make the set feel more complete.
The tool covers the main labeling lifecycle: input records, label definitions, artifact creation, and confidence evidence for low-confidence cases. Minor gaps exist around separate review or correction workflows, but the returned evidence mitigates this.