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Alternatives to laya-mlx-mcp

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    Related Servers

    • A
      license
      Not graded
      quality
      C
      maintenance
      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
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      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.
      3
      Do What The F*ck You Want To Public
    • A
      license
      Not graded
      quality
      A
      maintenance
      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.
      197 PyPI
      1
      Apache 2.0
    • A
      license
      A
      quality
      C
      maintenance
      An MCP server that answers typed noul/choice/score questions about a given state with probability distributions and confidence, for routing, escalation, and gating decisions. It runs on pluggable backends (local Needle 3 or Laya models, or any OpenAI-compatible endpoint) and logs every decision so recorded ground truth can be used to track calibration.
      3
      MIT

    TDQS

    A3.9/5.0

    Scored across 3 tools

    Disambiguation5/5

    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.

    Naming Consistency5/5

    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.

    Tool Count5/5

    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.

    Completeness4/5

    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.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues