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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds real value by disclosing the dataset scope (only RX 7900 XTX and RTX 3060 Ti measurements exist), which bounds what can be returned, but it omits row ordering, result limits, and what happens with non-matching filters.

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?

Two short sentences, no filler, with the core identity of the data front-loaded followed by the one parameter clarification that matters most. Slightly vague phrasing ('any of the given settings') costs it a point but nothing is wasted.

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?

For a six-parameter, zero-required, read-only filter tool with no output schema, the description should say what a probe row contains and how filters combine. It establishes provenance and the gpu matching rule but leaves the returned shape and filter interaction to inference.

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 description coverage is 0% across six parameters, so the description must compensate and it only partially does: it explains that gpu is a substring match with examples. dora, rank, quant, seq_len, and lora_dropout are left completely undefined beyond their bare names, and combination semantics (AND vs OR) are unstated.

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 names the resource precisely: raw measured probe rows as opposed to computed/estimated output, which implicitly separates it from siblings like vram_fit and vram_max_rank. It does not explicitly name those siblings, so the differentiation is left to inference.

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?

It says results can be filtered by 'any of the given settings', but gives no guidance on when to choose this tool over vram_fit or vram_max_rank, nor any prerequisites or exclusion conditions. The agent must guess that this is the raw-data fallback rather than a computed answer.

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