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

Annotations already cover read-only/destructive semantics, but the description adds what they cannot: per-call cost ($0.01 via x402 USDC or card key), the 25-call free tier, failed calls free, and an explicit reason why idempotentHint=false matters ("each call is charged, so repeats are not free"). That cost-and-repeat guidance is decision-relevant information an agent would otherwise have to discover by spending money.

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?

Purpose is front-loaded, followed by use cases, example, pricing, and safety note — a sensible order. Minor blemishes: a doubled period in the example ("within minutes..") and a dense pricing clause that packs several facts into one sentence.

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 one parameter, no output schema, and annotations already declaring safety, the description covers language, cost, and side-effect profile; the return value is sketched (label + confidence). It could be slightly more explicit about the exact response shape, but nothing needed to call 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 coverage is 100% and the single `text` parameter is already documented with its 4,000-character limit. The description reinforces the English-only constraint and supplies an example value, but adds no syntax or formatting detail beyond the schema — baseline 3 is appropriate.

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?

States a specific verb and resource — predicts positive/negative sentiment of English text with a confidence score — plus the underlying model (DistilBERT fine-tuned on SST-2). An agent can immediately distinguish it from generic siblings like ai_classify or ai_moderation because the output is a fixed two-way label with a score.

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?

"Use to triage reviews, support messages, social posts and replies" gives concrete when-to-use scenarios. It stops short of naming alternatives or exclusions — e.g. that non-English input should be routed through ai_translate first, or that multi-label taxonomy work belongs to ai_classify.

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