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

The description goes beyond the annotations by disclosing the paid nature of the call: $0.002 in USDC on Base per successful call via x402, and notably only charges on success. This is meaningful behavioral context that complements the readOnlyHint=false annotation rather than contradicting it. It omits error handling and return-shape detail, but the cost/auth disclosure is substantial.

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?

Three tight sentences: what it returns, where to use it, and what it costs. The output contract is front-loaded and nothing is wasted.

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 two-parameter, no-output-schema tool, the description covers the essentials: output semantics (ordered keywords), usage contexts, and cost. Minor gaps remain around failure behavior and rate limits, but an agent has enough to invoke it 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 description coverage is 100%, with both the text length cap (12,000 chars) and the count range (3-30, default 10) documented in the schema. The description adds no parameter-level meaning beyond that, so the baseline 3 is correct.

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 specific resource (keywords and key phrases) and its ordering (most relevant first), which is a concrete output contract. The four listed use cases (tagging, SEO, search indexing, topic detection) further separate it from siblings like ai_summarize-text, ai_classify, and ai_extract-json.

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?

It clearly states when to reach for it via the four application scenarios, giving an agent enough context to route between it and adjacent AI tools. It stops short of naming an alternative or stating when not to use it, so it is not fully explicit.

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