vercel-jev-mcp
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Alternatives to vercel-jev-mcp
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- AlicenseNot gradedqualityAmaintenanceEnables any MCP host to obtain typed judgments — yes/no, choice, and score — with calibrated probabilities instead of prose, exposed as three tools over stdio. It runs fully offline with synthetic answers by default, and only transmits state to TypeSafe or OpenRouter when live routing is explicitly configured, reporting exactly what leaves at startup.66 npmApache 2.0
- AlicenseAqualityCmaintenanceEnables running Jev AI typed decisions from any MCP client, returning choices, scores, or yes/no with calibrated probabilities.312 npmMIT
- AlicenseNot gradedqualityAmaintenanceExposes local MCP tools for typed, calibrated LLM decisions — gating risky actions, yes/no judging, classification, ordinal rating, and multi-question decisions in one pass. Runs on the model Devin's CLI already provides via ACP (or a fully offline local backend), so agents get calibrated confidence with no extra API key, no Ollama, and no waitlist.46 PyPI6MIT
- 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
- AlicenseNot gradedqualityBmaintenanceEnables agents to submit Jev-format decision questions and route them across free Workers AI Clef models with Jev fallback, exposing MCP tools for deciding, checking budget, viewing stats, and retrieving recent calls.MIT
- AlicenseNot gradedqualityCmaintenanceEnables MCP-capable agents to run TypeSafe's Jev judgment model as typed yes/no, choice, and score tools, with calibrated probabilities, confidence thresholds, escalation for uncertain or non-judgment tasks, and an optional action gate that fails open.1MIT
TDQS
Scored across 9 tools
The tools are largely distinct, but a few are deliberately composite or overlapping: jev_step is effectively jev_coding_loop plus jev_tool_route, and jev_gate bundles jev_review and jev_verify. An agent must read the long descriptions carefully to select the right orchestration recipe, so misselection is possible despite the clear use-case notes.
All tools share the consistent jev_ prefix and lowercase snake_case style, which makes the set feel predictable. However, the suffix pattern is mixed: verb-style names like jev_review, jev_verify, and jev_rank sit alongside noun-style names like jev_gate, jev_step, and jev_coding_loop, plus the compound noun jev_tool_route.
Nine tools is a well-scoped count for this kind of workflow server, and each tool maps to a recognizable phase: screening, ranking, evaluation, routing, stepping, review, verification, and final gating. None of the tools feel like filler, and the set is large enough to cover meaningful decisions without becoming unwieldy.
The toolset covers the full intended lifecycle for a decision/review server: untrusted text screening, candidate ranking, arbitrary typed evaluation, tool/step routing, next-action loop decisions, diff review, claim verification, and a combined final gate. The deliberate exclusion of file edits/execution is consistent with the stated purpose, and jev_evaluate acts as an escape hatch to prevent dead ends.