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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds genuinely useful behavioral facts beyond that: the 500-text cap and the $0.01 USDC per-call x402 cost, which an agent must know before invoking. It omits what happens above 500 texts or any latency/rate behavior.

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 dense sentences: purpose, scope, output shape, cost and cap, all front-loaded with no filler. Every clause carries information an agent needs.

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 read-only/open-world annotations, no output schema, and full schema coverage, the description supplies what is missing: output fields, the 500-text limit, accepted input format, and pricing. Only edge behavior (over-limit handling, default k) is left implicit.

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 both parameters (k, texts) are already documented in the schema, establishing a baseline of 3. The description adds only the 500-item ceiling for 'texts' and does not explain the '|' delimiter or the auto behavior of k beyond what the schema states.

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?

Specific verb+resource ('Cluster and label') with scope ('up to 500 texts') and a concrete preview of the output ('topics with an AI-written label, size, share and examples'). It is distinguishable from near-siblings like classify or chunk by describing grouping into topics, though it never names those alternatives explicitly.

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?

Usage context is implied only through example inputs (reviews, tickets, survey answers, search results). There is no statement of when to choose this over classify, dedupe, or chunk, and no prerequisites beyond the implicit cost note.

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