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Get hardware profile

get_hardware_profile
Read-onlyIdempotent

Reads chip, CPU cores, RAM, GPU/VRAM, and Apple Silicon generation as JSON to size local models and inform model selection.

Instructions

Read chip, CPU cores, RAM, GPU/VRAM and Apple Silicon generation for model sizing. Use recommend_model to turn this into a model pick. Read-only. Returns JSON hardware fields.

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv6.2.0

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so 'Read-only' adds no new information. The genuinely additive content is the enumeration of the hardware dimensions covered and the note that Apple Silicon generation is included, but it says nothing about failure modes on unsupported platforms.

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?

Three short sentences, front-loaded with the returned fields and followed by the hand-off. 'Read-only' duplicates the annotation and 'Returns JSON hardware fields' duplicates the existing output schema, so a little of the text does not earn its place.

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

Completeness4/5

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

With zero params, full annotation coverage and an output schema handling return values, the description only needs to state what is read and what to do next — both are present. The one meaningful gap is platform applicability, since Apple Silicon implies a macOS-only tool and the description never says so.

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, which is the baseline-4 case: there is nothing for the description to disambiguate. The description correctly implies a no-argument call and instead spends its words on what the call yields.

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?

A specific verb ('Read') plus an enumerated resource (chip, CPU cores, RAM, GPU/VRAM, Apple Silicon generation) and the reason for reading it ('for model sizing'). It is clearly distinguishable from sibling read tools like get_status or get_model_providers because it names the exact hardware fields returned.

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

Usage Guidelines4/5

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

The description routes the agent forward explicitly: 'Use recommend_model to turn this into a model pick.' That is a clear usage context with a named sibling. It stops short of stating exclusions (e.g. what to do on a non-Apple-Silicon host or when hardware data is unavailable), so it falls just under the top mark.

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