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

Goes well beyond the annotations by disclosing the credit model (1 credit per answered text; blanks and in-call duplicates free; re-asking in a later call is charged again), the model's limits (text only, no math/counting/dates, English best), internal chunking into calls of 40, and the requirement to embed the full definition of 'yes' in the question.

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?

Front-loaded with the core action and output, then use cases, the preference threshold, cost rules, and limits. Every sentence adds a distinct operational fact; nothing is padded or repeated.

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?

An output schema exists, yet the description still characterizes the return value (a number per text), and it covers cost, batching, model limits, and question-authoring requirements. Nothing an agent needs to invoke this 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%, so the baseline is 3, but the description adds meaningful semantics: the question must carry the complete yes/no definition because the model sees nothing else, and per-text cost depends on blank/duplicate handling. It still doesn't add much on api_key beyond the schema, keeping it short of a 5.

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 precise verb+resource: judging a batch of short texts against one yes/no question and returning a calibrated probability (0-1) per text, explicitly 'not generated prose'. The yes/no framing plus the output type distinguishes it from a multi-class or generation sibling at a glance.

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?

Gives explicit use cases (score, tag, filter, triage leads/tickets/reviews/emails) with concrete example questions, and a quantified decision rule: prefer it over judging yourself once there are more than ~25 texts. That is a genuine when-to-use threshold, not just a restatement of purpose.

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