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GPU Manager MCP

GPU Manager MCP

by quxlzx
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_gpu_health

Checks a GPU's thermal, memory, and power health using live and historical data to detect issues and diagnose problems.

Instructions

Health check: thermal/memory/power issues from live + historical data.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "gpu_id": "<gpu_id>"
    },
    "name": "get_gpu_health"
  }
}

Input Schema

NameRequiredDescriptionDefault
gpu_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden; it does disclose that data comes from both live and historical sources, which is real behavioral context. However, it never states whether the call is read-only, what the health verdict looks like, or how issues are reported.

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

Conciseness4/5

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

A single front-loaded line with no filler, and the scope (thermal/memory/power) is stated up front. It is terse to the point of being a fragment, but nothing is wasted.

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 and no annotations, the description must cover the whole contract, and it explains only the domains checked, not the shape or severity of the returned health result. Adequate to identify the tool, insufficient to predict its response.

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

Parameters2/5

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

Schema description coverage is 0% for the single gpu_id parameter, and the description adds no meaning about its format or source. The only required input is left entirely undocumented in both structured and prose form.

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 concrete purpose: a GPU health check covering thermal, memory, and power issues, drawn from live plus historical data. That aggregates the per-metric siblings (get_gpu_temperature, get_gpu_memory, get_gpu_power) into a distinct diagnostic role, though it never names those siblings explicitly.

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 phrase 'health check' implies a diagnostic use case, but there is no explicit guidance on when to prefer this over the individual metric tools or get_all_gpu_metrics. The agent must infer that this is the aggregated 'is something wrong' call.

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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