GPU Server
PYPI · GPU-SERVER
MCP server for GPU monitoring: nvidia-smi, VRAM, utilization, temperature
Available components
Last scanned (20 hours ago)
Scores higher than 68% of PyPI servers
Category Monitoring and Observability
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- No install/post-install scripts declared.Pass
- 1 of 29 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 0 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability65
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 108 tokens (~21/item across 5 items; 5 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management3
- Stability observed for 1 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 5 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 5 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 22 Oct 26 +30
- Injection markers: unverified → pass ▲ security
- First check of Judged manipulation: pass security
- Tool coverage: unverified → 100 ▲ functional
- MCP protocol: unverified → pass ▲ functional
- Stability: unverified → 0.03 ▲ functional
- First check of Schema quality: fail functional
- First check of Destructive annotations: pass functional
- First check of Schema quality: pass functional
- First check of Schema quality: good functional
- First check of Tool coverage: 100 functional
- 21 Oct 26 39
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 22 Oct 2026 · Analysed pypi/gpu-server@0.2.2
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
Dependencies 29 packages
| Packages resolved | 29 |
|---|---|
| No linked repository | 1 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
gpu_info ~21
GPU name, driver version, and CUDA version for each device.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
gpu_stats ~24
Full snapshot: info, VRAM, utilization, and temperature for every device.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
gpu_temperature ~17
Core temperature in °C for each device.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
gpu_utilization ~19
GPU core and memory utilization percentage for each device.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
gpu_vram ~27
VRAM total, used, free (MiB) and utilization % for each device.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
How do I install the GPU Server MCP server?
GPU Server runs locally as a PyPI package, launched with uvx gpu-server. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · gpu-server
claude mcp add moezdil-mcp-gpu-server -- uvx gpu-server
What is the GPU Server MCP server?
GPU Server is an MCP server listed in the public MCP registry as io.github.moezdil/mcp-gpu-server. MCP server for GPU monitoring: nvidia-smi, VRAM, utilization, temperature. This page covers its PyPI package (gpu-server).
Is the GPU Server MCP server safe to use?
GPU Server scores 69 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 22 October 2026. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the GPU Server MCP server expose?
GPU Server exposes 5 tools: gpu_info, gpu_utilization, gpu_vram, gpu_temperature, gpu_stats. Their descriptions and schemas cost roughly 108 tokens of context every time the server is loaded.
Is the GPU Server MCP server still maintained?
GPU Server is still listed as active in the MCP registry. We last reached this channel on 22 October 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the GPU Server MCP server under?
GPU Server declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.