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laya-mlx-mcp

by Blackphi6
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Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
LAYA_MLX_DTYPENoThe data type for the model, e.g., float16 or bfloat16
LAYA_MLX_MODELNoThe model checkpoint to use, default is aac6fef/laya-multilingual-mlx

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
laya_statusA

ロード済みモデルと実行環境の状態を返す。

laya_loadB

チェックポイントを事前ロードする。未指定時は環境変数または既定の multilingual を使う。

Args:
    model: Hugging Face のモデル ID またはローカルパス。例: aac6fef/laya-multilingual-mlx
    dtype: float16（既定）または float32
laya_predictA

状態に対して typed decision を実行する。

Args:
    state: 判定対象の文章。JSON オブジェクト／配列の文字列でも可。
    questions_json: 質問定義の JSON オブジェクト文字列。各値は type / instructions
        （choice・score は criteria）を持つ。
    model: 使うチェックポイント。省略時はロード済み、なければ既定モデル。
    dtype: float16 または float32。

Example questions_json:
    {
      "department": {
        "type": "choice",
        "instructions": "どの部署が対応すべきか？",
        "criteria": ["billing", "technical", "sales"]
      },
      "urgency": {
        "type": "score",
        "instructions": "緊急度は？",
        "criteria": ["not urgent", "soon", "critical"]
      },
      "refund": {
        "type": "noul",
        "instructions": "返金を求めているか？"
      }
    }

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

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

Resources