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Metal GPU Trace

de partprogramming09versión 0.22.0i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.partprogramming09/metal-gpu-trace

Instalar

Resumen

Descripción del creador (original en inglés)

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

Los tres pilares de PopNx MCP

Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

  • Conexión: Se ejecuta en tu computadora
  • Transporte: stdio
  • Categoría: Otros

Instalación

Elige la app de IA que usas y copia la configuración.

Antes de instalar

Necesitas

  • PyPIuv de Python instalado (para el comando uvx)

¿Qué app usas?

Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.

Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en 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"
      }
    }
  }
}

Ejecuta esto en tu terminal

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

Completa los valores vacíos antes de ejecutarlo.

Abrir en Cursor

O copia la configuración tú

Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json

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

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Abrir en VS Code

O copia la configuración tú

Pégalo en .vscode/mcp.json de tu proyecto

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

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.

Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.

Valores que debes completar

  • 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

Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.

Qué puede hacer

Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub partprogramming09, dueña del repo

  • … Aún sin revisar

    Sigue en mantenimiento

    Todavía no hemos leído este repo en GitHub

  • … Aún sin revisar

    Tiene licencia

    Todavía no hemos leído este repo en GitHub

  • – No aplica

    El endpoint responde

    Este se ejecuta en tu computadora; no hay endpoint en internet

  • – No aplica

    Inicio de sesión con OAuth

    Se ejecuta en tu computadora y usa ajustes locales en su lugar

  • … Aún sin revisar

    Sin instrucciones ocultas en las descripciones de herramientas

    Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet

Comparado con otros en Otrosi

  • Más estrellas que el 0% de los otros 32537 servidores de esta categoría
  • Publicado en el registro más recientemente que el 97% de los otros 32537 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 32537 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Preguntas frecuentes

¿Es seguro Metal GPU Trace?

Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.

¿Qué claves o códigos necesita?

El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.

¿Se ejecuta en mi computadora o en la nube?

Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (0.22.0) llegó al registro ayer.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.

¿Qué puedo usar en su lugar?

En la misma categoría: Spec Driven Development, Remote Desktop Commander, TF Rooms

Alternativas

Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.

Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi

Spec Driven Development

de formulahendry

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

Se ejecuta en tu computadora★ 440MITActualizado hace 14 meses
Instalar

Remote Desktop Commander

de desktop-commander

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

Se ejecuta en la nube★ 211Actualizado ayer
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TF Rooms

de tfrooms.com

Live room availability, rates and stay quotes for 12 New Leaf motels in Twin Falls, Idaho.

Se ejecuta en la nubeActualizado hoy
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README del creador

Abrir el README

Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.

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

Solo se muestra el principio; lee el resto en el repo

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