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

Annotations already declare the safe read-only, non-destructive, non-open-world profile, and the description adds real value beyond them: search does not consume lead allowances, and email/phone are masked in results. It still omits return-field detail, but the allowance and masking disclosures are meaningful behavioral context.

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 in the first sentence and every subsequent sentence carries distinct information (dataset routing, interpretation caveat, allowance cost, masking, pagination). No filler.

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?

For a zero-required-parameter search with no output schema and fully documented params, the description covers routing, cost, and result masking. It is close to complete, though it does not describe the returned record fields.

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%, so every parameter (page, per_page, gender, country, keyword, category, industry) is already documented. The description restates the filterable fields and notes the 50-per-page cap but adds no format or syntax detail beyond the schema, 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 (Search) and resource (LastDatabase Hot Lead dataset) and enumerates the filterable facets. Naming the dataset explicitly helps route it among the sibling search_* tools, though it never directly contrasts itself with the closest alternative, search_mixed_leads.

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 when the customer specifically requests records from the Hot Lead dataset' gives a clear selection condition tied to the dataset name. No when-not guidance or named alternative tool is provided, so it stops short of full routing guidance.

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