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

Beyond the readOnly/idempotent/non-destructive annotations, the description discloses provenance ('facts come from each company's own site'), non-endorsement ('payouts ... are not offers'), and ranking policy ('alphabetical or by text relevance ... never by referral fee'), which materially affects how an agent should present results. It does not mention pagination or total-count behavior, keeping it just short of a 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?

Two front-loaded sentences, with the core purpose and routing stated before any caveats. Every clause — provenance, non-offer disclaimer, ordering fairness, sibling pointer — carries distinct information and none is redundant.

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?

All seven parameters are optional and fully documented in-schema, and the detail parameter explains the response shape in lieu of an output schema. Only minor gaps remain (no mention of result truncation or what happens when max_results exceeds available entries).

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% and the enum/field descriptions (e.g. region outside_US, industry 'omit to see them') already carry the parameter meaning. The description's note that a query switches ordering to text relevance adds modest semantic value but does not re-specify individual parameters, 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?

States a specific verb and resource — searching DataWorth AI's directory of companies/programs that license operating records to AI developers — and names the sibling tools it is not (check_fit, get_buyer implicitly via detail). An agent can distinguish it from every sibling without opening a schema.

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?

Explicit routing advice: 'Use this to list or compare buyers; to compare one company's facts with programs' eligibility rules, use check_fit.' The schema further supplies when-not guidance (omit industry to see buyers that take any industry), leaving no inference needed.

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