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Glama

host_status

Read-onlyIdempotent

Query live GPU and host metrics (utilization, VRAM, temperature, fan, power, service states, loaded models, warnings) to assess box health or confirm memory fits a model.

Instructions

Read-only GPU and host status from the operator's Control panel (lmstudio.example.com): GPU utilization, VRAM used/total/free, temperature, fan and power, which processes hold VRAM, systemd service states, the models LM Studio has loaded, and any warnings. REACH FOR THIS when the user asks how the GPU/box is doing, or before offering a local-model decision that may not fit in memory (ask_lms already uses the same reading for its room check). Best-effort: an unreachable control page returns a status error, never a crash.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.16.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces this with 'Read-only'. It additionally discloses the best-effort behavior: 'an unreachable control page returns a status error, never a crash.' This adds meaningful failure-mode context beyond the structured annotations.

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?

The description is dense but well-organized: core purpose and metrics first, usage trigger second, and failure caveat last. Every sentence adds useful information, and there is no filler or repetition of schema contents.

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

Completeness5/5

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

Even without an output schema, the description lists all relevant data categories and the error behavior, making it clear what the agent can expect. The combination of usage context, sibling relationship, and failure semantics fully covers what an agent needs to invoke this zero-parameter read-only tool correctly.

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 and the schema has no properties, so there is no parameter burden for the description to carry. The baseline for zero-parameter tools is 4, and the description appropriately spends no space on parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description begins with a specific verb and resource: 'Read-only GPU and host status from the operator's Control panel'. It enumerates the exact metrics returned (GPU utilization, VRAM, temperature, fan, power, process holders, systemd states, loaded models, warnings), which leaves no ambiguity about what the tool does. It also distinguishes itself from sibling tools by explicitly referencing ask_lms and the local-model memory context.

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

Usage Guidelines5/5

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

The description contains an explicit trigger: 'REACH FOR THIS when the user asks how the GPU/box is doing, or before offering a local-model decision that may not fit in memory.' It also names the relevant sibling (ask_lms) and clarifies its relationship, giving the agent a concrete routing rule rather than leaving usage to inference.

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