GPU MCP Server
Website
MCP server for NVIDIA GPU metrics give AI agents real-time access to GPU utilization, memory, temperature, and power
Is GPU MCP Server safe? Thin or incomplete trust evidence. Review carefully before production use.
Compare GPU MCP Server
How does it stack up against its MCP Servers neighbours?
Pick any agent to compare →In detail: GPU MCP Server scores 51.1/100 (Grade C), ranked #972 of 1374 tracked open-source AI agent projects, on evidence coverage C (2 of 5 independent signal types). The public evidence: no package-provenance attestation found; OSSF Scorecard rates its supply-chain practices 5.9/10; 72% of recent commits are signed; last pushed 2026-10-24. Every point is earned from checkable signals — never paid placement. How scoring works →
How GPU MCP Server could raise its score
Each line changes one public signal and recomputes with the live scoring function. The gains don't add up exactly, because the score is capped near the top.
- Raise the OSSF Scorecard from 5.9 to 9.0lowest checks: CII-Best-Practices 0, Fuzzing 0, Signed-Releases 0 +6.5 → 57.6 C · #418 of 778
- Sign every commit72% signed today +1.4 → 52.5 C · #523 of 778
Chain of custody
Scanners check what the code says. This traces who ships GPU MCP Server and whether that has changed: from the source repository, through how changes are reviewed and released, to the code you run. These are the checks for OWASP ASI04 Agentic Supply Chain Vulnerabilities.
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Source Partly signed
github.com/pmady/gpu-mcp-server, Apache-2.0, last pushed 2026-10-24. 72% of the last 100 commits carry a verified signature; the rest can't be tied to a verified identity.
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Review and release Mixed
OpenSSF Scorecard rates the repository's practices 5.9/10 (scanned Nov 9, 2026). The checks that decide who can get a change released:
- Code Review5
- Branch Protectionn/a
- Signed Releases0
- Dangerous Workflow10
- Token Permissions0
- Pinned Dependencies1
All 18 Scorecard checks
Binary-Artifacts 10Branch-Protection -1CI-Tests 10CII-Best-Practices 0Code-Review 5Contributors 3Dangerous-Workflow 10Dependency-Update-Tool 10Fuzzing 0License 10Maintained 8Packaging 10Pinned-Dependencies 1SAST 10Security-Policy 4Signed-Releases 0Token-Permissions 0Vulnerabilities 6 -
Published packages None
Distributed as the container image ghcr.io/pmady/gpu-mcp-server:v0.1.0; HVTracker doesn't check that registry's source link yet.
Changes to this chain
No change to package provenance, package source links, Scorecard coverage or license in HVTracker's daily snapshots of GPU MCP Server.
Evidence behind this score
Coverage C: 2 of 5 independent evidence types found.
- GitHub repository data
- Package downloads (missing)
- Supply-chain checks
- Public actions (missing)
- Community mentions (missing)
Verify this score yourself
Every build re-issues GPU MCP Server’s score as an Ed25519-signed credential, valid for 7 days. You can check it offline against HVTracker’s published key; if anyone changes a number after signing, verification fails.
What this score doesn’t check
It covers who ships the code, not what the code does. It doesn’t read tool descriptions or prompts for injected instructions, watch runtime behaviour, or find bugs nobody has disclosed yet. For that, run a content scanner before you connect it, such as Cisco MCP Scanner or Snyk Agent Scan.
How GPU MCP Server compares in MCP Servers
- #543 Glif 51.3 +0.2
- #544 Microsoft MCP Server for Enterprise 51.2 +0.1
- #545 GPU MCP Server 51.1 this agent
- #546 Joplin MCP Server 51.1 ±0
- #547 Ai Memory MCP 51.1 ±0
Bars show each HVTrust score; the tick marks GPU MCP Server’s 51.1.
Where the 51.1 comes from
HVTrust dimensions vs the MCP Servers average
51.1 / 100 · 100.0% confidenceGPU MCP Server MCP Servers average (778 agents)
Quick Trust Read
How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption. Grade C reflects the trust score band: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Evidence coverage C is separate — it grades how many independent signal types back the score (2 of 5), so a high score on thin evidence stays visible. Full methodology →
Rank Trend
Activity & Reach
Analysis
Activity Inputs
35.2 / 100Common questions about GPU MCP Server
Does GPU MCP Server publish package provenance?
Does GPU MCP Server have an OpenSSF Scorecard?
Is GPU MCP Server actively maintained?
What license does GPU MCP Server use?
Are GPU MCP Server's commits signed?
Not a safety endorsement. HVTracker describes what public signals show, not whether a project is safe for your use case. Run your own security review before adopting in production.
AI agent surface
MCP, providers, tool surface
These runtime-trust fields — detected from public repo docs and manifests — contribute a bounded adjustment to this project's HVTrust score alongside supply-chain evidence. The exact values each field can add or subtract are documented in the methodology → Compare this surface across every listed agent in the capability matrix →
- MCP signal live
- External deps live
- Tool / plugin surface live
- Package provenance drift live
Detected changes to GPU MCP Server's runtime surface and supply-chain posture, from daily public-signal snapshots. A change here means our detectors see something different — a genuinely changed capability, or better evidence of an existing one.
Maintain GPU MCP Server?
For maintainers
HVTrust scores GPU MCP Server from public signals only — we never contact maintainers first. If a signal is wrong, stale, or missing (provenance you publish, a Scorecard you run, signed releases), tell us and we'll review it. Corrections are public and tracked on GitHub.
Reputation Timeline
Signal history
Embed Badge Badge guide for maintainers →
For maintainers
[](https://hvtracker.net/agents/gpu-mcp-server)
<a href="https://hvtracker.net/agents/gpu-mcp-server"><img src="https://hvtracker.net/badge/gpu-mcp-server.svg" alt="HVTrust"></a>
Other agents in MCP Servers
GitHub REST API (repo, commits, stars, forks, license) · OpenSSF Scorecard CLI
Each agent's signals refresh once daily across 6 staggered batches. Methodology v4.4 · Raw JSON