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

Annotations already cover the safety profile (readOnlyHint, closed-world), and the description adds genuinely non-obvious behavior: measured RigBase results when available, otherwise a labelled VRAM-fit estimate, plus source credit and URLs for measured rows. This tells the agent how much to trust the answer, which annotations and schema cannot. It stops short of saying what happens when the GPU or model is unknown.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences, front-loaded with the purpose, then the measured-vs-estimate distinction, then the quant advice. No filler and nothing padded.

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

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

With no output schema, the description does the necessary work of explaining what comes back: measured rows with attribution or a clearly labelled estimate. That is sufficient for an agent to call it correctly, though error/unknown-input behavior and any rate or lookup limits are unaddressed.

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

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

Schema coverage is 100%, so all four parameters are already documented with examples and defaults. The description only reinforces the quant guidance ("advises against quants below Q4"), which the schema's quant field already states. Baseline 3 is appropriate.

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

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

The description states a specific verb and resource ("Ask whether a given GPU setup can run a model"), which is clearly the inverse direction of the sibling best_gpu_for_model. However, it never names that sibling or otherwise explicitly distinguishes itself, so the differentiation is inferable 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.

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

The framing "Ask whether..." implies a compatibility-check use case, but there is no explicit when-to-use or when-not-to-use guidance and no mention of best_gpu_for_model / search_builds as alternatives. An agent must infer that this is the GPU-then-model direction rather than the model-then-GPU direction.

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