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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context: independent per-label scoring, the 0.7 keep threshold, and the 'possibly none' outcome. It doesn't describe the exact output shape, but an output schema exists, so that burden is reduced. Minor gap: no mention of how instructions interact with scoring, but overall strong.

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 sentences, all information-dense and front-loaded. The first sentence establishes the core behavior and contrast with the sibling; the second gives use cases; the third and fourth give operational parameters. No filler or repetition of schema content.

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 multi-label classification tool with a rich schema (100% coverage), an output schema, and safety annotations, the description covers the essential decision points: when to use it, how it differs from classify_texts, how scoring works, and how to cap results. Nothing an agent needs to invoke it correctly is missing.

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. The description adds value by explaining the threshold behavior (>= 0.7 kept) and the 'possibly none' outcome, which clarifies the semantics of max_labels and labels. It doesn't add much beyond the schema for instructions, but the baseline is 3 and the description does compensate with useful behavioral detail.

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 ('classify'/'tag'), a resource ('texts'), and the key differentiator: every label that applies, with independent 0-1 scores. It explicitly contrasts with classify_texts and gives concrete use cases (topics of an article, components touched by a ticket), so an agent can distinguish it from siblings without opening the schema.

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 description explicitly says 'Like classify_texts, but...' and explains when to use this tool ('Use this for tagging... where one answer is not enough'). It also gives practical guidance on setting max_labels and the 0.7 threshold. This is clear when-to-use guidance with an explicit alternative named.

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