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GPU Manager MCP

GPU Manager MCP

by quxlzx
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_gpu_history

Fetch historical samples for a selected GPU metric over a time window to monitor utilization, memory, temperature, power, or clock trends.

Instructions

Historical samples for one metric over a time window. Metrics: utilization_percent, memory_used_bytes, memory_total_bytes, memory_free_bytes, temperature_celsius, power_watts, clock_mhz.

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>",
      "metric": "<metric>"
    },
    "name": "get_gpu_history"
  }
}

Input Schema

NameRequiredDescriptionDefault
gpu_idYes
metricYes
duration_secondsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

No annotations exist, so the description carries the full disclosure burden. It reveals the valid metric vocabulary, which is genuinely useful, but says nothing about sampling interval, maximum lookback, whether samples are downsampled, or the shape and ordering of returned data.

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 short sentences, front-loaded with the core behavior, followed by an inline enumeration of valid metric names. No filler and nothing buried.

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 time-series tool with no annotations and no output schema, the description leaves out the essentials an agent needs to call it confidently: sampling granularity, window bounds, and the returned sample structure. Only the metric vocabulary is covered.

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 description coverage is 0%, so the description must compensate. It does enumerate the acceptable metric values, which the schema does not, but gpu_id and duration_seconds (default 300) receive no explanation, including any permitted range or behavior when the window exceeds retention.

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 the resource ('historical samples') and the scope ('one metric over a time window'), which clearly separates it from the current-snapshot siblings like get_gpu_utilization or get_gpu_temperature. It never names a sibling explicitly, so the differentiation is inferred from the word 'historical' 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 implied: use this when you need a time series for a single metric. There is no explicit when-not guidance, no mention that multi-metric history would require repeated calls or a different tool, and no stated limits on the time window.

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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