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

With no annotations, the description carries the full burden and does well: it discloses the 5,000-character input ceiling, the model backing it (Llama 4 Scout on Workers AI), the output cardinality caps (10 keywords, 10 entities, 1-3 topics), and the paid nature of the call ($0.003 USDC on Base via x402). It omits error behavior, latency, and whether the charge applies to failed calls.

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 sentences, front-loaded with the output shape before the operational metadata (model, size limit, price). Every clause carries information; only the model name is arguably marginal for tool selection.

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?

No output schema exists, so the description must describe return values, and it does so with cardinalities and entity type vocabulary. Cost and payment rail are disclosed, which matters for an x402-priced tool. The remaining gap is the actual response structure (field names, score scale) which an agent may need.

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?

There is a single parameter at 100% schema description coverage, and the description only restates the 5,000-character limit already present in the schema. No additional meaning (encoding, language handling, truncation behavior) is added beyond the structured field, so the baseline 3 applies.

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 concrete outputs (keywords with relevance scores, typed named entities, 1-3 broad topics) so an agent can tell this is an extraction tool rather than a summarizer or sentiment classifier. It stops short of naming a specific sibling as the alternative, but the output inventory is specific enough to differentiate.

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?

There is no when-to-use or when-not-to-use guidance, and no routing against nearby siblings such as ai_summarize, ai_sentiment, or ai_embeddings. The agent must infer from the output list alone that this is the right tool for keyword/entity extraction.

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