token-optimizer-jev-mcp
Related Servers
Alternatives to token-optimizer-jev-mcp
No user-submitted related servers found.
Related Servers
- AlicenseAqualityAmaintenanceEnables sending a shared context with multiple independent questions to TypeSafe's Jev model via the public OpenJEV API, returning typed judgments and probabilities (choice, score, or yes/no) instead of prose.532 npm1MIT
- 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
- AlicenseNot gradedqualityAmaintenanceEnables any agent to ask typed questions (Noul, Choice, Score) against Jev's decision model and receive structured answers with probabilities, confidence, and an auditable act/review/abstain decision.60 npm2MIT
- 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
- AlicenseAqualityAmaintenanceLets MCP clients ask typed questions — yes/no, choice, or score — about structured state, including images, and receive calibrated probabilities from System One decision models such as TypeSafe Jev and Cloudflare Clef. Supports TypeSafe, OpenRouter, and local providers selectable per request, with input validation and cost reporting.6223 npmMIT
- AlicenseNot gradedqualityBmaintenanceEnables MCP clients to consult TypeSafe's Jev through a judge tool, answering narrow typed questions with calibrated probabilities instead of prose.32 npm1MIT
TDQS
Scored across 6 tools
jev_ask can handle label sets, rubrics, and yes/no questions, so it overlaps with jev_choice, jev_score, and jev_noul for single-question use. The specialized tools are individually clear, but an agent could reasonably hesitate between the generic ask tool and the specialized one.
All tools share a consistent jev_ prefix and lowercase snake_case style, with list_models and server_info following a recognizable pattern. The names score, ask, choice are concise, though jev_noul is cryptic and not clearly verb- or noun-shaped.
Six tools is well-scoped for a decision-serving MCP: four distinct question modes plus two informational endpoints. Each tool earns its place, and the count is comfortably inside the expected range.
The surface covers all stated capabilities: rubric scoring, label choice, yes/no probability, mixed parallel questions, model metadata, and server configuration. The explicit design limitation that Jev does not generate text explains why generation/rewrite tools are absent, leaving no obvious dead ends.