laya-mlx-mcp
Related Servers
Alternatives to laya-mlx-mcp
No user-submitted related servers found.
Related Servers
- AlicenseAqualityCmaintenanceEnables local Laya-MLX typed decisions (classification, scoring, risk routing, and yes/no) for coding agents like Codex, Claude Code, DeepSeek Harness, and Pi via MCP, running entirely on Apple Silicon Macs.11MIT
- AlicenseNot gradedqualityCmaintenanceLaya-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 gMIT
- AlicenseNot gradedqualityAmaintenanceProvides 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.Apache 2.0
- AlicenseNot gradedqualityCmaintenanceEnables local, read-only Laya typed decisions for Codex, providing classification, scoring, truth-probability, batch decision, and token-budget planning tools on Apple Silicon.MIT
- AlicenseNot gradedqualityDmaintenanceMCP server that enables local Apple on-device Foundation Model access via any MCP client, supporting text generation, structured output, and multi-turn chat on macOS.2MIT
- AlicenseNot gradedqualityCmaintenanceEnables 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.1MIT
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: status reports environment state, load prepares a checkpoint, and predict runs inference. There is no functional overlap between any pair.
All tools follow a uniform laya_ prefix with a concise verb suffix (status, load, predict), using consistent snake_case throughout. The pattern is predictable and easy to extend.
Three tools form a tight, well-scoped set for model serving: check status, load a model, and run predictions. No tool feels extraneous, and the count is sufficient for the domain.
The core lifecycle of loading and using a model is covered, including a status check for runtime awareness. A minor gap is the lack of an explicit unload or model listing operation, but these are not essential for the stated purpose.