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 one useful behavioral promise: output contains no fabricated scoring or ranking. It does not cover what happens with unknown model names, duplicate entries, or lookup failures, which for a comparison tool is a real gap.

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?

One sentence, front-loaded with the verb and resource, and every clause earns its place — the range, the distinctness rule, and the no-fabricated-ranking guarantee each add information.

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?

An output schema exists and annotations cover the safety profile, so the description doesn't need to explain return values. Still missing: which attributes are compared, and behavior when a model is not found — details an agent needs to interpret a comparison result correctly.

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 0%, but the description compensates well for a single-parameter tool: it says 2–4 entries, that they must be distinct, and that they must be 'exact' model identifiers — the exactness point meaningfully rules out fuzzy matching, which the schema's plain string type does not convey.

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?

States a specific verb and resource: compare 2–4 models side by side. The negative scoping ('no invented performance score or compatibility ranking') sharpens what kind of comparison it is, but the description never names an alternative like compute_capability or get_accelerator, so sibling differentiation is left implicit.

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 2–4 distinct-model constraint implies the multi-model use case, and 'distinct' excludes duplicate entries. However there is no explicit guidance on when to pick this over compute_capability or repeated get_accelerator calls, which an agent has to infer from the name alone.

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