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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuinely useful context beyond the annotations: it describes the return shape (fit verdict, memory arithmetic, decode-speed range with a confidence label) and the underlying model (memory sized from all parameters, speed from active parameters). There is no mention of caching, rate limits, or how the confidence label is derived, so it is not exhaustive, but it is well above the annotation baseline.

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?

Four tight sentences with no filler: usage trigger first, then required inputs, then return shape and computational basis, then exclusions. Every sentence earns its place and the most important routing information is front-loaded.

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?

The tool has an output schema, so the description is not obliged to explain return values, yet it still sketches what comes back (verdict, memory arithmetic, speed range with confidence label). Combined with annotations, required-parameter documentation, and sibling differentiation, an agent has everything it needs. Minor gap: it does not explain how quantisation and context_length affect the answer, though the schema gives defaults and ranges.

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 100%, so the schema already documents all four parameters, their defaults, and constraints. The description only names the two required ids that come from search_catalog and does not add syntax or format detail for quantisation or context_length. With the schema carrying the full parameter burden, this is the baseline 3.

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+resource ('check whether one specific model runs on one specific machine') and explicitly scopes it to a single hardware/model pair, distinguishing it from siblings like models_for_hardware (many models on one machine) and compare_hardware (side-by-side). It also names the required inputs and the computational basis (memory from all parameters, speed from active parameters), so the agent knows exactly what this tool answers.

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 opening sentence gives an explicit trigger ('Use this when the user asks whether one specific model runs on one specific machine, at what memory cost, and roughly how fast'), and the closing sentence states exclusions: 'It does not recommend purchases and does not run anything.' Both when-to-use and when-not-to-use are covered without requiring inference.

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