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GPU Manager MCP

GPU Manager MCP

by quxlzx
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

compare_gpus

Compare specs and live telemetry for multiple GPUs side by side to evaluate differences in utilization, memory, temperature, power, and clocks.

Instructions

Side-by-side comparison of specs and live telemetry for several GPUs.

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_ids": "<gpu_ids>"
    },
    "name": "compare_gpus"
  }
}

Input Schema

NameRequiredDescriptionDefault
gpu_idsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the output content ('specs and live telemetry'), which implies a read-only operation. It omits anything about how invalid or unavailable GPU IDs are handled, whether missing GPUs are skipped, or the shape of the side-by-side result.

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 front-loaded sentence with no filler; the comparison purpose is stated immediately. It is perhaps too terse given the tool's role, but nothing in it 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?

Without an output schema or annotations, the description is the sole source of behavioral detail, and it says what data is produced but not how it is structured or limited. For a simple one-parameter read tool this is roughly adequate, but constraints on the ID list and result semantics are missing.

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

Parameters3/5

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

Schema coverage is 0% for the single parameter, so the description must compensate. The phrase 'for several GPUs' does signal that gpu_ids is a multi-value list of GPU identifiers, but the ID format, accepted count range, and behavior with duplicate or unknown IDs remain undocumented.

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 verb and resource ('side-by-side comparison of specs and live telemetry' for 'several GPUs'), which clearly separates it from siblings like get_gpu_info (single GPU) and list_gpus (enumeration). It doesn't explicitly name those siblings, but the multi-GPU comparison framing is distinctive enough for correct selection.

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 infer this is for comparing multiple GPUs at once rather than inspecting one. There is no explicit when-to-use vs get_gpu_info or get_all_gpu_metrics, and no stated minimum number of GPUs or prerequisites.

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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