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

Annotations cover the safety profile (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false), and the description adds a genuinely new trait: a paid call costing $0.002 USDC on Base via x402. That cost/auth mechanism is not derivable from annotations or schema. It doesn't disclose latency or retry/refund behavior, keeping it below 5.

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: capability first, then use cases, then cost. Every sentence carries distinct information with zero padding.

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 no output schema, the description compensates by describing the return shape (label, reason, ranking). Paid-call mechanics and input constraints are covered, so 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 coverage is 100%, meaning both 'text' (8,000 char limit) and 'labels' (2-20) are already documented in the schema. The description's restatement of the 2-20 label constraint adds nothing new, 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 opens with a precise verb and method ('Zero-shot classification'), names the inputs, and states the outputs ('best label, a short reason and a ranking'). This clearly separates it from adjacent AI siblings like ai_sentiment or 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 gives concrete when-to-use contexts ('routing tickets, tagging content, intent detection and moderation queues'), which is more than most siblings offer. It stops short of naming alternatives or exclusions, so it's clear but not fully routing.

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