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GPU Manager MCP

GPU Manager MCP

by quxlzx
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_all_gpu_metrics

Retrieve a full telemetry snapshot for every GPU at once to monitor utilization, memory, temperature, power, clocks, and processes across NVIDIA, AMD, and Intel hardware.

Instructions

Full telemetry snapshot for every GPU at once.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_all_gpu_metrics"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are supplied, so the description carries the full behavioral burden. 'Snapshot' implies a point-in-time read, but it does not state whether the call is read-only, what 'full telemetry' includes, whether it is expensive to run against many GPUs, or how results are returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single short sentence, front-loaded with the scope-defining phrase 'for every GPU at once'. There is no wasted wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description should say enough about the returned snapshot for an agent to trust it, and it does not. Given 17 overlapping siblings, it also leaves the agent guessing whether this subsumes the per-metric tools or returns something different.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there are no parameter semantics to document; the baseline for a no-param tool is a 4. Nothing in the description misleads about inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource (full telemetry) and scope (every GPU at once), which implicitly distinguishes it from the per-GPU siblings like get_gpu_utilization and get_gpu_memory. It stops short of naming any sibling, but the 'all at once' phrasing makes the aggregate-vs-single distinction readable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'for every GPU at once' clause hints at when to prefer this over the many single-metric tools, but gives no explicit when-to-use or when-not guidance and no mention of alternatives such as calling get_gpu_utilization/get_gpu_memory individually.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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