jev-mcp
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No user-submitted related servers found.
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- AlicenseAqualityCmaintenanceEnables typed, calibrated judgment calls through classify, score, check, and batched ask tools, each returning full probability distributions for programmatic decisions.51MIT
- AlicenseNot gradedqualityBmaintenanceEnables agents to verify claims with evidence-based truth scores and confidence levels by running a deterministic pipeline of evidence lanes and adversarial checks.26MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to verify claims deterministically by computing arithmetic, ratios, and dates and matching statements against provided sources, returning a confidence ladder of certain, source-backed, or unverifiable.MIT
- FlicenseNot gradedqualityBmaintenanceEnables AI agents to score their outgoing responses against groundedness and prompt-injection risks mid-turn, returning allow, warn, or block verdicts before the response reaches the user.9 npm-
- AlicenseAqualityCmaintenanceEnables agents to get fast, calibrated probabilistic answers from Jev (Typesafe AI) to yes/no, scale, or choice questions about provided material, without using a generative model.1MIT
- AlicenseAqualityBmaintenanceEnables production-grade answer verification for LLM agents by independently re-checking answers with a configurable verifier model, enforcing confidence policies, and producing Ed25519-signed, auditable verification results with optional web search and knowledge-base evidence.3MIT
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: verifying claims against evidence, screening text for prompt injection and relevance, and ranking candidates against a query. There is no meaningful overlap or ambiguity between them.
All tool names follow the same 'jev_' prefix plus a single verb in snake_case: jev_verify, jev_screen, jev_find. The pattern is uniform and predictable.
Three tools is a reasonable, focused scope for this server. Each tool covers a distinct high-level capability and none feel redundant or excessive.
The set covers the main apparent workflows: verification, guardrail screening, and semantic finding. Minor gaps could exist around configuration or explanation, but the core surface feels complete for its focused purpose.