Skip to main content
Glama
Sign In
GPU Manager MCP

GPU Manager MCP

by quxlzx
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_system_gpu_summary

Retrieve a system-wide overview of all GPUs with one-line key metrics for quick utilization, memory, temperature, power, and clock checks.

Instructions

System-wide overview: every GPU with one-line key metrics.

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_system_gpu_summary"
  }
}

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?

With no annotations at all, the description carries the full behavioral burden. It discloses the coarse output shape ("one-line key metrics") and the fact that all GPUs are returned, but says nothing about which metrics are included, whether data is live or cached, or how large the response can get on multi-GPU hosts.

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 front-loaded sentence with the scope first and the output shape second; no filler, no restatement of the tool name.

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?

There is no output schema, so the description is the only place a caller can learn what comes back, and "key metrics" is left undefined. For a zero-argument summary tool that is mostly adequate, but it leaves the agent guessing whether utilization, memory, temperature or all of them are present.

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 the schema has nothing to explain and the baseline of 4 applies. The description correctly implies no filtering is possible, though it does not say so explicitly.

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?

"System-wide overview: every GPU with one-line key metrics" states the resource (GPUs), the scope (system-wide, every GPU), and the shape of the result (one line of key metrics each). It is clear on its own, but it never distinguishes itself from list_gpus or get_all_gpu_metrics, so an agent must infer the difference.

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: "overview" suggests a quick first-pass survey across all devices rather than deep per-GPU inspection. There is no explicit when-to-use, no mention of when to prefer list_gpus or get_all_gpu_metrics, and no stated prerequisites, which matters given 17 closely related GPU siblings.

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

Deploy Server

Other Tools