mcp-laya
Related Servers
Alternatives to mcp-laya
No user-submitted related servers found.
Related Servers
- AlicenseAqualityCmaintenanceProvides agents with fast, typed, calibrated decision tools for classification, scoring, yes/no checks, and gating risky tool calls.5447 npm2MIT
- AlicenseAqualityBmaintenanceEnables MCP clients to run fast, local, non-autoregressive System-1 decisions on text, including classification, triage, routing, and safety gating, via six typed tools with probabilistic outputs.208Apache 2.0
- 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.137 PyPI6MIT
- AlicenseNot gradedqualityAmaintenanceProvides coding agents with typed classification, yes/no checks, scoring, ranking, and question-answering tools that return calibrated probabilities for fast, reliable decisions.725 npm44MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to run local, model-backed decisions on short passages and routing choices, and to check whether exactly supplied text supports a scientific claim, returning the full probability distribution, review-policy status, and provenance details. All evaluation stays on the local machine with no cloud inference, and flagged results are surfaced for human review rather than treated as conclusions.MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to perform typed decisions (choice, score, noul) over text, emails, tickets, or JSON documents, with automatic language routing and source ranking across 100+ languages via a single forward pass.Apache 2.0
TDQS
Scored across 8 tools
classify, score, and check are single-question wrappers around decide's choice/score/noul modes, so an agent choosing between decide and a specialized tool may face ambiguity for a one-off task. Their descriptions clearly separate the single-vs-multiple use cases, but the underlying semantics overlap.
All tool names are lowercase and start with an imperative verb, with compound names following verb_noun (detect_language, explain_routing, list_models). The single-word verbs (decide, classify, score, check, triage) are a minor stylistic deviation from the compound pattern but remain predictable.
Eight tools is a reasonable size for a decision-model server and stays inside the ideal 3-15 range. The count is slightly padded, though, because classify/score/check are specialized versions of decide's three question types.
The set covers general and specialized decisions, ready-made triage presets, language detection, routing explanation, and model listing—no obvious dead ends for inference workflows. Minor gaps include no queryable preset list or way to register custom presets, but these can be worked around.