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

Adds substantial context beyond the annotations: credit cost of 1 per answered text, free blanks and duplicates, internal chunking into calls of 40, and explicit model limits (text only, no math/counting/dates, English works best). The cost disclosure is especially valuable given readOnlyHint=false, since the operation consumes credits.

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?

A single dense paragraph that front-loads the purpose and return shape, then layers examples, preference guidance, cost, and limits with no redundant sentences. Every clause carries information an agent would otherwise have to guess.

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?

Complete for a batch scoring tool: purpose, scale constraints, cost model, capability limits, and usage threshold are all stated, and the output schema exists so return-value structure need not be explained. 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%, so the baseline is 3, but the description adds cross-parameter semantics: levels are low-to-high and shown in concrete form, and the question should say what is being scored. This clarifies how texts, levels, and question fit together rather than merely restating field docs.

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: place a batch of short texts on an ordered scale and return a probability-weighted position, most likely level, and confidence. Explicitly says it returns numbers, not generated prose, which separates it from prose-generating siblings like hunch_ask and hunch_quote.

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 concrete use cases (sentiment, fit scoring, any low-to-high rating) and an explicit prefer-this-over-doing-it-yourself threshold ('more than about 25'). It does not, however, route the agent among siblings such as hunch_pick or hunch_multi, which handle adjacent selection-style judgments.

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