jev-mcp
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- AlicenseNot gradedqualityBmaintenanceEnables MCP clients to consult TypeSafe's Jev through a judge tool, answering narrow typed questions with calibrated probabilities instead of prose.42 npm1MIT
- 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
- AlicenseAqualityBmaintenanceEnables MCP clients to get calibrated probability judgments for choice, score, yes/no, and batch classification questions via the TypeSafe System One API.5120 PyPI3MIT
- AlicenseNot gradedqualityAmaintenanceEnables MCP clients to submit bounded semantic-uncertainty judgments to the pinned TypeSafe Jev API, with tools for yes/no, choice, and score evaluations plus optional evidence or context selection.MIT
- AlicenseAqualityCmaintenanceEnables running Jev AI typed decisions from any MCP client, returning choices, scores, or yes/no with calibrated probabilities.316 npmMIT
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusing its purpose with another tool. The tool's description clearly defines its unique role of evaluating content against typed questions and returning structured probabilities.
A single tool named 'evaluate' cannot be inconsistent with anything else, and the name is a clear, verb-based descriptor that matches its function. No pattern mixing or naming conflicts exist to penalize.
The server exposes only one tool for what appears to be a broad evaluation domain. The description suggests a wide range of uses, but one tool provides no supporting workflows, making the server feel too thin for its apparent scope.
The tool covers a single evaluation action, but the broader domain of evaluation likely includes question/template management, batch evaluation, or result history. As it stands, agents can perform isolated evaluations but have no way to manage or reuse evaluation setups, creating dead ends.