jev-netlify-mcp
Related Servers
Alternatives to jev-netlify-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityAmaintenanceEnables Claude Code or any MCP client to ask TypeSafe's Jev for calibrated, typed judgments (probabilities, choices, scores) instead of prose, with local caching and cost tracking.1MIT
- AlicenseAqualityCmaintenanceEnables running Jev AI typed decisions from any MCP client, returning choices, scores, or yes/no with calibrated probabilities.316 npmMIT
- AlicenseNot gradedqualityBmaintenanceExposes bounded TypeSafe Jev/System One decision primitives as MCP tools, enabling agents to make choices, scores, yes/no judgments, and batched decisions over remote HTTPS with shared state.MIT
- 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
- AlicenseNot gradedqualityAmaintenanceLets 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.470 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables MCP hosts to query Jev's typed decision model—yes/no, choice, and score—with calibrated probabilities, while defaulting to an offline mock and disclosing all egress unless explicitly enabled.106Apache 2.0
TDQS
Scored across 2 tools
The two tools are clearly distinct: one evaluates text against typed questions, while the other lists available models. There is no overlap in purpose or parameters that would cause misselection.
Both names use a consistent jev_ prefix and snake_case, but jev_evaluate is action-oriented while jev_models is a resource listing. This is a minor deviation from a pure verb_noun pattern.
Two tools is thin for a server, though each tool has a valid role in a minimal evaluation API wrapper. It is borderline rather than clearly well-scoped or severely mismatched.
The core evaluation workflow and model discovery are covered, but there are minor gaps such as no batch evaluation, validation, or result-retrieval tooling. These are workable omissions for a thin MCP wrapper.