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GPU Manager MCP

GPU Manager MCP

by quxlzx
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

detect_gpu_bottleneck

Analyze sampled GPU history to identify compute, thermal, power, or memory bottlenecks and indicate when evidence is insufficient for a reliable diagnosis.

Instructions

Heuristic bottleneck analysis (compute/thermal/power/memory) from sampled history. Reports when evidence is insufficient.

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": "detect_gpu_bottleneck"
  }
}

Input Schema

NameRequiredDescriptionDefault
gpu_idYes
window_secondsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
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 usefully discloses that the analysis is heuristic, based on sampled history, and can report insufficient evidence, but it does not state read-only behavior, side effects, required data availability, or any execution characteristics.

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?

Two compact sentences with no wasted wording. The core purpose is front-loaded, and the important caveat about insufficient evidence is included without bloating the definition.

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

Completeness2/5

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

The tool has two undocumented parameters, no annotations, and no output schema, yet the description does not explain parameter semantics or the shape of the diagnosis returned. It gives useful purpose and caveat information but is not complete enough for reliable invocation.

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%, so the description must compensate for both parameters. It only hints at sampled history, which loosely relates to the window, but does not explain gpu_id, window_seconds, the default duration, or expected units.

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?

The description names a specific analysis: heuristic bottleneck detection across compute, thermal, power, and memory using sampled history. It is distinguishable from sibling tools that fetch raw metrics or health, though it does not explicitly name those alternatives.

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

Usage Guidelines2/5

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

There is no explicit guidance on when to use this diagnostic tool versus the many sibling tools for raw GPU metrics, history, health, or recommendations. The mention of insufficient evidence implies a diagnostic context, but the agent is left to infer when this tool is appropriate.

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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