原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
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Metal GPU Trace

作者:partprogramming09版本 0.22.0i★ –i在你的电脑上运行
注册名称io.github.partprogramming09/metal-gpu-trace

安装

概览

作者提供的说明(英文原文)

Measure Wine and macOS programs' GPU use on Apple silicon with Metal System Trace

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 其他

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: METAL_TRACE_OUT, METAL_TRACE_WINE

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "metal-gpu-trace": {
      "command": "uvx",
      "args": [
        "metal-gpu-trace"
      ],
      "env": {
        "METAL_TRACE_OUT": "your-value",
        "METAL_TRACE_WINE": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add metal-gpu-trace --env METAL_TRACE_OUT=your-value --env METAL_TRACE_WINE=your-value -- uvx metal-gpu-trace

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "metal-gpu-trace": {
      "command": "uvx",
      "args": [
        "metal-gpu-trace"
      ],
      "env": {
        "METAL_TRACE_OUT": "your-value",
        "METAL_TRACE_WINE": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "metal-gpu-trace": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "metal-gpu-trace"
      ],
      "env": {
        "METAL_TRACE_OUT": "your-value",
        "METAL_TRACE_WINE": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • METAL_TRACE_OUT env
    Absolute path of the directory where recordings go
  • METAL_TRACE_WINE env
    Wine directory holding bin/wine, used when a recording names none

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 partprogramming09 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32403 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32403 个服务器的 100% 更新
  • 基础检查结果等于或优于此分类中其他 32403 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Metal GPU Trace 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.22.0)于今天进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, LickityClick

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者:formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新
安装

LickityClick

作者:lickity.click

Create, edit and track LickityClick short links, campaigns and click stats from your AI assistant.

电脑或云端今天更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

metal-gpu-trace

Measure how a Wine or macOS program uses the GPU on an Apple silicon Mac, from the command line or through an AI coding agent: GPU time per frame, frame pacing, FPS and memory, recorded with Instruments' Metal System Trace; whether a translation layer (DXMT, Mullion, MoltenVK) splits the work into too many short encoders; Metal .gputrace captures read without Xcode; and Apple's published facts on each GPU family to read the numbers against. Python 3.9+ standard library, zsh, and Xcode (for xctrace). MIT license. Español.

FileWhat it does
perf_record.shRecords a program for N seconds with Metal System Trace and writes _ .json (GPU ms per frame, frame intervals, FPS, Metal and resident memory) plus per-frame and memory CSVs. Deletes the .trace afterwards unless KEEP_TRACE=1.
perf_report.pySummarizes an existing .trace the same way.
gputrace_read.pyReads a Metal .gputrace capture without Xcode: command buffers, encoders, draws, debug groups. Call names marked probable are not confirmed.
data/apple-gpu-families.jsonApple7 to Apple10: chips, what each family adds, limits that differ, with Apple's sources.
metal_trace_mcp.pyAn MCP server over all of the above.
test_metal_trace_mcp.pySelf-check without Instruments: python3 -I test_metal_trace_mcp.py prints ok; pytest runs the same tests.

Recording

PERF_OUT=~/perf-runs zsh perf_record.sh game-d3d12 30 /path/to/wine ~/.wine /path/to/game game.exe

PERF_OUT=~/perf-runs zsh perf_record.sh app-metal 30 native - /path/to/app ./app

Arguments: label, seconds, a Wine directory holding bin/wine (or native), the Wine prefix, the working directory, the program and its arguments. Without PERF_OUT, runs go to ./perf-runs. Reading the trace afterwards takes about a minute on an M1 Pro (87 s for a 10 s recording); through the MCP server the whole call times out after 10 s per recorded second plus 120 s, and an overrun recording leaves no files behind. One recording runs at a time; keep other GPU work off the Mac while it records.

Under Wine, give it 30 s: Wine's start-up takes part of the recording. A recording with under 100 frames is too short to compare.

Disk use

Each recording keeps a JSON summary and three CSVs, about 2 MB for 10 s (most of it the Metal memory samples). The .trace (tens to hundreds of MB) is deleted unless KEEP_TRACE=1. KEEP_RUNS=N keeps only the newest N recordings. PERF_MEMORY=0 skips the Metal memory table, the slowest to read: the summary then has no metal_memory_mib and there is no memory CSV (41 % faster to summarize a 626 MB trace). The MCP tools perf_record and perf_baseline_record offer the same as memory=false.

MCP server

Install

The server runs with uvx from PyPI, with nothing to clone (brew install uv installs it). Recording also needs Xcode installed (xcrun xctrace version prints a version).

uvx metal-gpu-trace

This is the command each client below runs. Desktop apps do not see Homebrew's PATH: give them the full path that which uvx prints (/opt/homebrew/bin/uvx on Apple silicon). uvx keeps the version it first downloaded; run the command once with --refresh after uvx to get the latest. uvx metal-gpu-trace@0.4.0 pins a version.

The server reads these environment variables:

VariableWhat it sets
METAL_TRACE_OUTWhere recordings go. Give an absolute path: the default, ./perf-runs, depends on the client's working directory.
METAL_TRACE_WINEThe Wine directory (holding bin/wine) that perf_record uses when the call names none.
WINEPREFIXThe Wine prefix used when the call names none; ~/.wine otherwise.
METAL_TRACE_READMore directories the tools may read, separated by :.
METAL_TRACE_LOGoff silences the one-line stderr log per call.
KEEP_TRACE1 keeps each recording's .trace, for trace_tables, trace_query or Instruments.
KEEP_RUNSKeeps only the newest N recordings.
PERF_ENVNAME=VALUE pairs, separated by spaces, set for the recorded program.
PERF_INSTRUMENTSInstruments added to the Metal System Trace template, separated by ; (xcrun xctrace list instruments names them); their tables need the .trace, so pair it with KEEP_TRACE=1.
Claude Code
claude mcp add --scope user metal-trace -e METAL_TRACE_OUT=$HOME/perf-runs -e METAL_TRACE_WINE=/path/to/wine \

-- uvx metal-gpu-trace claude mcp list

claude mcp list shows metal-trace as connected. --scope user makes it available in every project; leave it out to add it to the current project only.

Windows

On Windows the server records native programs (perf_record_windows.py); no Wine and no Metal. METAL_TRACE_WINE is not needed: perf_record and perf_attach use native. METAL_TRACE_READ is separated by ;.

Frames come from Intel's PresentMon console application (MIT): one row per Present() of the process, with MsBetweenPresents and the GPU time MsGPUTime. Download PresentMon- -x64.exe from its releases, check its SHA-256 against the one GitHub shows, and call the presentmon_install tool, or put it in %LOCALAPPDATA%\metal-gpu-trace or on PATH, or point PRESENTMON_EXE at it. The user must be an administrator or in the "Performance Log Users" group. Without PresentMon the summary falls back to DWM composition intervals (gpu.source is dwm-composition): they cover the whole desktop, miss games in exclusive full screen, and carry no GPU time. With neither a frame log, PresentMon nor DWM samples, a recording fails; it never invents frames.

What cannot be measured is null, not zero: the 1% low needs 100 frames and the 0.1% low 1000; stutter rate and jitter need two frames; thermal_throttling_detected needs GPU times; a drawstress log holds one mean per line, so its summary (gpu.frame_times) has no frame-level percentiles, stutter or jitter. DWM keeps only intervals between consecutive composed frames and drops a jump of several frames. perf_diagnose calls a large working set with uneven frame times MEMORY_PRESSURE_SUSPECTED: a suspicion, since memory bandwidth is not measured.

winget install --id=astral-sh.uv -e

claude mcp add --scope user metal-trace -e METAL_TRACE_OUT=%USERPROFILE%\perf-runs -- uvx metal-gpu-trace claude mcp list

To run from a clone instead of PyPI (needs Python 3.9 or newer):

claude mcp add --scope user metal-trace -e METAL_TRACE_OUT=%USERPROFILE%\perf-runs -- python C:\path\to\metal-gpu-trace\metal_trace_mcp.py

In JSON clients use "command": "uvx" (or the full path where uvx prints) and Windows paths with doubled backslashes. Claude Desktop reads %APPDATA%\Claude\claude_desktop_config.json. Run the Windows tests with python -m pytest test_perf_record_windows.py.

Claude Desktop, Cursor and other JSON clients

These apps do not expand ~ or $HOME: write absolute paths.

{

"mcpServers": { "metal-trace": { "command": "/opt/homebrew/bin/uvx", "args": ["metal-gpu-trace"], "env": { "METAL_TRACE_OUT": "/absolute/path/to/perf-runs", "METAL_TRACE_WINE": "/absolute/path/to/wine" } } } }

ClientFile
Claude Desktop~/Library/Application Support/Claude/claude_desktop_config.json (Settings, Developer, Edit Config); restart the app
Cursor~/.cursor/mcp.json
VS Code.vscode/mcp.json in the project; the top key is servers instead of mcpServers, and the server adds "type": "stdio"
Codex CLI

In ~/.codex/config.toml:

[mcp_servers.metal-trace]

command = "/opt/homebrew/bin/uvx" args = ["metal-gpu-trace"] env = { METAL_TRACE_OUT = "/absolute/path/to/perf-runs", METAL_TRACE_WINE = "/absolute/path/to/wine" }

Tools
ToolReads or records
perf_recordRecords a program and returns its summary; when it ends it stops every program of its Wine prefix, or every process named like a native program. counters=true also records Metal GPU Counters: gpu_counters (the limiters, utilization, occupancy and memory bandwidth of the GPU, with the main limiter) in the summary, at about 40 s of read-back per recorded second; not under Windows
perf_attachRecords a program that is already running, by pid or name, and leaves it running
perf_runsLists recordings, newest first, one page at a time, whether each kept its .trace, and the translation layer it ran on (a fingerprint of its Direct3D DLLs, and its Wine)
perf_compareCompares two recordings: headline numbers, encoders, GPU idle time and each encoder label's GPU ms per frame, largest change first; flags those under 100 frames. markdown=true adds the numbers as Markdown tables for a PR comment; skip_s leaves out the first seconds of each recording (start-up) and recomputes the GPU numbers, hitches and Metal memory without them. perf_gate and perf_panel take skip_s too
perf_baselineSaves several equal recordings as a named reference and returns the range each number spans across them and spread_pct, how much equal runs differ (the noise a change must beat); alone, lists the references. skip_s saves each run without its first seconds and perf_check applies the same cut to the run it judges
perf_baseline_recordRecords the same program count times (default 3) with perf_record's arguments and saves the runs as a reference in one call. skip_s as in perf_baseline
perf_cold_warmRecords the same program twice in a row and says how many more shader-compile hitches the first run has than the second (first_seen_hitches), with each run's hitches, hitches per minute and graphics compiler time; the Metal shader cache is not cleared between them
perf_abRecords the same program with two environments (env_a, env_b: NAME=VALUE pairs) in the order A B B A and says per metric (FPS, 1 % low, GPU ms, stutter) whether B is better, worse or the same: better or worse only when every B value is outside A's range
perf_sweepRecords the same program once per value of one variable (dim, size, example, api or env:NAME), 2 to 8 values, rounds times (every other round in reverse order), and returns per value the median FPS, 1 % low, GPU ms per frame and stutter; with 3 or more numeric values it fits GPU ms per frame on the value (fixed ms and ms per draw for dim) with its r2
perf_checkJudges a recording against a reference: regressed when FPS falls under the range by more than margin_pct (default 3), GPU ms per frame rises over it, or encoders or command buffers per frame exceed it; improved or same otherwise, with the reasons and the full comparison
perf_gateChecks a recording against budgets you set (FPS and 1% low floors; stutter, p95 GPU ms, hitch and Metal MiB ceilings) and returns pass or fail with each limit and value; a missing number fails
perf_historyFollows FPS, 1% low, GPU ms, stutter, hitches and Metal MiB across the runs whose name starts with a prefix, oldest first, with the change from first to last

| perf_panel | Writes .panel.html: a page with GPU time per frame and its hitches, the numbers, Metal memory and, for two runs, A against B. It has no server and loads nothing from the network; the viewer hides or reorders widgets and exports the layout as JSON. With --http, `/panel#token=...&runs=a,b

只显示了开头部分;其余内容请在仓库中阅读

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