"laya" matching MCP servers:

Enables MiMo Desktop to run a typed-decision engine through local stdio MCP, returning choice/score/noul predictions with calibrated confidence for auto-execution, LLM review, or escalation—without generating text.

Enables local bounded decision-making through MCP using a resident Core ML model, with tools for single decisions, batched decisions, and conservative context filtering.

Enables MCP-compatible agents to make millisecond automated decisions by exposing calibrated probabilities and decision contracts from the Laya System-1 model through MCP, REST, and CLI interfaces.

Provides fast, offline typed decision tools (classify, score, check, triage) to agents via the Model Context Protocol, enabling safe local inference across 100+ languages with calibrated confidence and no text generation.

Enables local, LLM-free probabilistic decisions (choice, classification, scoring, yes/no) with real probabilities in 100+ languages, as a drop-in replacement for jev-local tools.

Enables coding agents to call Laya's decision-model presets as tools for tasks like request classification, guard checks, content moderation, ticket triage, and custom evaluations.

Provides an MCP interface to the Laya decision model, enabling typed queries (yes/no, multiple choice, score) with preflight token-budget reporting, honest confidence calibration, and structured error handling.

Enables MCP clients to ask typed questions about text or JSON and receive structured answers with probabilities, and also exposes a REST endpoint for non-MCP tools.

Enables 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.

Enables typed decisions (choice/score/noul) from Laya-MLX over MCP on Apple Silicon, without text generation.

Enables fully offline, token-free screening of text through a local Laya typed-decision model, exposing prompt-injection/jailbreak detection plus structured choice, score, and yes/no judgments in about 55ms per call. It also reports service status with measured metrics and known limitations.

Enables local, read-only Laya typed decisions for Codex, providing classification, scoring, truth-probability, batch decision, and token-budget planning tools on Apple Silicon.

Enables clients to submit typed System One decisions against the Laya model and check service readiness over stdio or HTTP SSE, with the same configuration, validation, and API-key protection as the REST API.

Enables AI agents to run local, CPU-only inference for decision tasks such as choosing among labels, estimating yes/no probability, and scoring ordered descriptions. Exposes these capabilities through MCP tools, REST/Swagger endpoints, and a browser test console without GPUs, paid APIs, or databases.

Enables 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.

Enables agents to get fast, calibrated typed decisions from a shared Laya decision service: classifying text, scoring rubrics, answering batches of yes/no checks, and running question packs. Exposes guards and tool-call helpers that gate proposed tool calls, screen tool results for injected instructions, check replies and input for leaks or jailbreaks, and validate individual form fields and whole submissions with pass, fix, review, or reject outcomes.

An MCP server that brings a local MLX-powered decision layer to coding agents, letting them choose short workflow actions such as task routing, risk checks, next steps, and test/review decisions without cloud APIs.

Laya-MLX as an MCP server (and plain-HTTP API). It answers typed decisions — choose an option / score a rubric / is this true? — on your own machine (Apple Silicon / MLX), with no cloud and ~10ms after warm-up. Not a chatbot. No token-by-token text, no JSON that can break. One forward pass returns a structured answer you can branch on. Ideal for routing, triage, classification, lead scoring, and g

Enables agents to resolve ambiguous decisions by returning calibrated probabilities for each candidate action, along with a gate that signals whether to act on the top option or escalate, all via a cheap local or remote backend.

Enables local typed decision-making over MCP Streamable HTTP, running Laya and Von models to turn arbitrary state into choice, score, and yes/no answers with probabilities.