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MacOS Resource Monitor MCP Server

@Pratyay

代码仓库

基本信息

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其他

传输方式

stdio

作者

Pratyay

关于 MacOS Resource Monitor MCP Server

暂无概览

MacOS Resource Monitor MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mac-monitor-mcp": {
      "command": "uv",
      "args": [
        "tool",
        "install",
        "."
      ]
    }
  }
}

MacOS Resource Monitor MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MacOS Resource Monitor MCP Server?

MacOS Resource Monitor MCP Server is a Model Context Protocol server that identifies resource-intensive processes on macOS across CPU, memory, and network usage. It is designed for users who want to monitor system resources via an LLM or other MCP client.

How to use MacOS Resource Monitor MCP Server?

Install globally with uv tool install . or run from source with python src/mac_monitor/monitor.py. Then start the server, which exposes three tools: get_resource_intensive_processes(), get_processes_by_category(), and get_system_overview().

Key features of MacOS Resource Monitor MCP Server

  • Lists top 5 most resource-intensive processes per category.
  • Provides paginated, sortable process listing for CPU, memory, network.
  • Returns comprehensive system overview with CPU, memory, disk, network stats.
  • Uses built-in macOS utilities (ps, lsof) for real-time snapshots.
  • Designed for LLM integration via the Model Context Protocol.

Use cases of MacOS Resource Monitor MCP Server

  • System performance monitoring and bottleneck identification.
  • Resource usage trending and capacity planning.
  • Troubleshooting system slowdowns and performance issues.
  • Getting a quick system health overview via an LLM.
  • Automating resource analysis as part of a larger MCP workflow.

常见问题

1

How does this server differ from macOS Activity Monitor?

It exposes data via the MCP protocol for LLM consumption rather than a graphical interface, and uses `ps` and `lsof` for real-time snapshots.

2

What are the runtime requirements?

macOS operating system, Python 3.10 or later, and the MCP server library (`mcp`).

3

How is the data collected and where does it come from?

Data is collected on each tool invocation using built-in macOS command-line utilities `ps` and `lsof`; no persistent storage is used.

4

Are there any known limits?

The `get_resource_intensive_processes()` tool only returns the top 5 processes per category. Network monitoring counts connections without measuring bandwidth.

5

What transport or authentication does it use?

The server uses the Model Context Protocol standard; the README does not specify transport details or authentication requirements.

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