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get_gpu_info

Retrieve hardware and driver information for a specified GPU ID to identify model, driver version, and capabilities before configuring workloads or diagnosing issues.

Instructions

Hardware and driver information for one GPU.

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_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
gpu_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations supplied, the description carries the full disclosure burden and only conveys that the tool reads static hardware/driver data. It does not state that the call is read-only and side-effect free, what happens when gpu_id is invalid or the GPU is absent, or whether driver info can be stale or requires elevated privileges.

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?

One short, front-loaded sentence with no filler or redundancy. Its brevity is a virtue structurally, though it borders on under-specification rather than crispness.

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?

For a tool with no annotations, no output schema and an entirely undocumented required parameter, the description leaves too much unsaid — neither the returned hardware/driver fields nor failure behavior is covered. It is adequate only for the simplest possible caller who already knows the API's identifier conventions.

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% and the single gpu_id parameter has no title-level explanation beyond its name. The phrase 'for one GPU' only weakly establishes that gpu_id selects a single device; it gives no hint about accepted identifier form (index, UUID, PCI bus ID) or whether list_gpus should be called first to obtain a valid value.

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 concrete verb-and-resource pair ('Hardware and driver information') and pins the scope to 'one GPU', which implicitly separates it from the per-metric siblings like get_gpu_utilization, get_gpu_memory and get_gpu_temperature. It stops short of naming any sibling explicitly, so the differentiation is inferred rather than stated.

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?

Usage is only implied: an agent can reason that static hardware/driver specs come from here while dynamic metrics come from the other get_gpu_* tools, but the description never says 'use this when you need static specs rather than live metrics' or excludes any alternative. No prerequisites or call-ordering guidance is given.

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