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check_system_environment

Diagnose Apple Silicon compatibility and available memory to assess readiness for local mlx-lm model deployment.

Instructions

現在のシステム環境(Apple Siliconか、空きメモリが何GBあるかなど)を診断します。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function that executes the 'check_system_environment' tool logic. It calls process_manager.get_system_info() and returns the system info as JSON.
    if name == "check_system_environment":
        info = process_manager.get_system_info()
        return [types.TextContent(type="text", text=json.dumps(info, indent=2))]
  • The get_system_info() method that collects system environment data (OS, architecture, memory, Python version) using psutil and platform modules.
    def get_system_info(self) -> dict:
        """現在のシステム状態(メモリ、アーキテクチャなど)を取得する"""
        mem = psutil.virtual_memory()
        return {
            "system": platform.system(),
            "machine": platform.machine(),
            "total_memory_gb": round(mem.total / (1024 ** 3), 2),
            "available_memory_gb": round(mem.available / (1024 ** 3), 2),
            "python_version": platform.python_version()
        }
  • The tool definition/schema registered for 'check_system_environment' - empty input schema, described as a system environment diagnostic tool.
        name="check_system_environment",
        description="現在のシステム環境(Apple Siliconか、空きメモリが何GBあるかなど)を診断します。",
        inputSchema={
            "type": "object",
            "properties": {},
        },
    ),
  • The @server.list_tools() decorator registers all tools including 'check_system_environment' via the handle_list_tools function.
    @server.list_tools()
    async def handle_list_tools() -> list[types.Tool]:
        """AIエージェントに提供するツールの一覧とスキーマを定義します"""
        return [
            types.Tool(
                name="check_system_environment",
                description="現在のシステム環境(Apple Siliconか、空きメモリが何GBあるかなど)を診断します。",
                inputSchema={
                    "type": "object",
                    "properties": {},
                },
            ),
            types.Tool(
                name="check_llm_status",
                description="指定されたポートでサーバーがリッスンしているか(稼働中か)を確認します。",
                inputSchema={
                    "type": "object",
                    "properties": {
                        "port": {"type": "integer", "description": "確認するポート番号"}
                    },
                    "required": ["port"],
                },
            ),
            types.Tool(
                name="list_running_servers",
                description="現在バックグラウンドで稼働しているすべてのローカルLLMサーバー(ポート番号とモデル名)の一覧を取得します。",
                inputSchema={
                    "type": "object",
                    "properties": {},
                },
            ),
            types.Tool(
                name="search_mlx_models",
                description="Hugging Faceからダウンロード可能なMLXフォーマットのLLMモデルを検索・リストアップします。",
                inputSchema={
                    "type": "object",
                    "properties": {
                        "search_query": {
                            "type": "string",
                            "description": "検索キーワード(例: 'llama', 'qwen')。未指定の場合は人気のMLXモデルを返します。"
                        },
                        "limit": {
                            "type": "integer",
                            "description": "取得する最大件数。デフォルトは10。"
                        }
                    },
                },
            ),
            types.Tool(
                name="download_model",
                description="Hugging Faceから指定されたMLXモデルを事前にダウンロードし、ローカルにキャッシュします。大きなモデルの起動前の準備に利用します。",
                inputSchema={
                    "type": "object",
                    "properties": {
                        "model_name": {
                            "type": "string",
                            "description": "ダウンロードするモデル名 (例: mlx-community/Llama-3-8B-Instruct-4bit)"
                        }
                    },
                    "required": ["model_name"],
                },
            ),
            types.Tool(
                name="launch_llm_server",
                description="mlx_lm.server をサブプロセスとしてバックグラウンドで起動します。空きメモリが少ない場合は起動が拒否されます。",
                inputSchema={
                    "type": "object",
                    "properties": {
                        "model_name": {
                            "type": "string",
                            "description": "起動するモデル名 (例: mlx-community/Llama-3-8B-Instruct-4bit)",
                        },
                        "port": {"type": "integer", "description": "サーバーを起動するポート番号"},
                        "memory_requirement_gb": {
                            "type": "number",
                            "description": "起動に必要な空きメモリの目安(GB)。未指定時はデフォルトで 4.0GB。"
                        }
                    },
                    "required": ["model_name", "port"],
                },
            ),
            types.Tool(
                name="restart_llm_server",
                description="指定されたポートで稼働しているサーバーを一度停止し、再起動します。モデルの切り替えなどにも使用できます。",
                inputSchema={
                    "type": "object",
                    "properties": {
                        "port": {"type": "integer", "description": "再起動するサーバーのポート番号"},
                        "model_name": {
                            "type": "string",
                            "description": "(オプション)新しく起動するモデル名。省略した場合は現在そのポートで稼働しているモデルをそのまま再起動します。"
                        },
                        "memory_requirement_gb": {
                            "type": "number",
                            "description": "(オプション)起動に必要な空きメモリの目安(GB)。未指定時はデフォルトで 4.0GB。"
                        }
                    },
                    "required": ["port"],
                },
            ),
            types.Tool(
                name="shutdown_llm_server",
                description="指定されたポートで稼働しているローカル LLM サーバープロセスを安全に終了させます。",
                inputSchema={
                    "type": "object",
                    "properties": {
                        "port": {"type": "integer", "description": "終了させるサーバーのポート番号"}
                    },
                    "required": ["port"],
                },
            ),
        ]
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses the tool's purpose (diagnose system environment) but does not mention side effects, permissions, or whether it is destructive. The diagnostic nature implies a read-only operation, but this is not explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is front-loaded with the main action ('診断します'). It is concise and contains no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description adequately conveys the tool's purpose for a simple diagnostic tool. However, it does not explain the output format or return value, which is a gap given the lack of an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are no parameters, and schema coverage is 100% trivially. Per guidelines, baseline score is 4. The description adds context about the type of diagnostic information, which is helpful.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states it diagnoses the current system environment, specifically mentioning Apple Silicon and free memory. This clearly distinguishes it from sibling tools that deal with LLM status, servers, and models.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use or alternatives are provided. However, the context of sibling tools and the description implies it should be used to check system hardware before running models. This is adequate but lacks explicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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