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Workloadtruth MCP server

by RudrenduPaul·io.github.RudrenduPaul/workloadtruth·v0.1.4
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Classifies GPU workloads as inference or training from telemetry alone via MCP tools.

A92/100grade A
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Safety scan
A92/100

full report

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0 stars11 downloads/wk

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If you have run it, two minutes of your experience saves the next person an afternoon.

Workloadtruth tools (3, 1 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • classify_workload

    Classify the current GPU workload as TRAINING, INFERENCE, or IDLE.

  • run_benchmarkwrite action

    Run the evasion-robustness benchmark against synthetic telemetry.

  • verify_audit_log

    Verify the hash chain of the local audit log has not been tampered with.

Public scan report

scanner v0.1.9 · 2026-09-23 · same rubric, same numbers if you re-run it

no findings
  • Code scan18 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 29 days ago15/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Overall 92/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

What the publisher says

From the Workloadtruth repository's README, as published. We do not edit it. Read it on GitHub

WorkloadTruth

<!-- mcp-name: io.github.RudrenduPaul/workloadtruth --> <!-- Ownership-proof string for registry.modelcontextprotocol.io publishing. Do not remove. -->

Install • Quickstart • CLI reference • Comparison • FAQ

Classify a GPU workload as TRAINING, INFERENCE, or IDLE from telemetry alone. No code changes to the workload, no self-reported job labels.

Every GPU scheduler in common use today, including run:ai, Slurm, and Kubernetes GPU operators, asks you to declare whether a job is training or inference at submission time. None of them check. WorkloadTruth reads GPU telemetry (utilization, memory pattern, power draw) and answers the question independently, so a mislabeled or misbehaving job doesn't go unnoticed.

Install

# Real NVIDIA GPU telemetry (requires an NVIDIA driver on the host)
pip install "workloadtruth-cli[nvml]"

# Try it without a GPU, using the synthetic backend
pip install workloadtruth-cli

# npm launcher (thin wrapper around the PyPI package, see "Why two registries")
npx workloadtruth-cli --help

[!NOTE]

The npm package is a launcher, not a standalone install. npx workloadtruth-cli execs the real workloadtruth binary from PATH, so the PyPI package (pip install workloadtruth-cli) must already be installed first.

Quickstart

# No GPU required. Classify a synthetic "training" telemetry trace.
$ workloadtruth classify --backend synthetic --profile training --samples 10 --interval 0
workload_type : TRAINING
confidence    : 1.00
gpu_index     : 0
samples       : 10 over 9.0s
reasons:
  - avg GPU utilization 87.8% >= training threshold 65.0%
  - low GPU utilization variance (std=3.4) <= training ceiling 15.0
  - memory growing 120.0 MiB/sample >= training threshold 5.0
  - low power-draw variance (std=9.4W) <= training ceiling 25.0W

# Real hardware
$ workloadtruth classify --backend nvml --samples 10 --interval 1 --json

--json on every command switches to machine-readable output for scripts and agents.

Quick summary

  • Use it for: catching cost-misallocated GPU jobs (a job billed as low-priority "inference" that's actually running full training) and unauthorized workload changes (an inference endpoint that starts training on live traffic without sign-off)
  • What it's not: a compliance or regulatory-audit tool. No regulation currently requires this kind of monitoring, see What WorkloadTruth is not below
  • Prior art: builds on and cites arXiv:2606.19262 (ICML 2026), see Relationship to prior research

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Install directly

claude mcp add workloadtruth -- uvx workloadtruth-cli
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Workloadtruth: common questions

Is Workloadtruth MCP server safe?
Yes, by our scan: it is graded A (92/100). Read the Workloadtruth safety report
How do I install Workloadtruth?
It runs on your machine. Copy the Claude Code, Claude Desktop or Cursor config from the install section.
Does Workloadtruth need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is Workloadtruth maintained?
The last commit was 30 days ago (2026-08-25). The latest release is v0.1.4.

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