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

Annotations already mark the operation read-only (readOnlyHint=true), and the description adds useful behavior: results are the exact engine answers, answers are trimmed to chars, and filtering by substring/engine is available. It also discloses what data is returned without contradicting the annotations.

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?

The description is compact, front-loads the core purpose, and then uses a dense field list that conveys return shape in a single breath. No sentence is wasted and the unit context is included without padding.

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?

Because there is no output schema, the description carries the responsibility of explaining the return shape, and it does: engine, model, date, naming/samples, live-search vs memory, competitors, fan-out searches, and Google demand. Combined with complete parameter schema coverage, an agent has enough to select and call the tool correctly.

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 all four parameters are already documented in the schema. The description's mention of filtering by question substring or engine and trimming to chars mostly restates existing parameter descriptions and does not add new semantic detail.

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?

Description clearly identifies a specific resource: exact verbatim answers from each engine for each tracked buyer question in the latest measurement. It distinguishes itself from siblings by framing the result as the 'receipt behind every score' and enumerating answer-level fields, which separates it from get_citation_sources, get_context, and get_mentions.

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?

The phrase 'receipt behind every score' implies use when raw answer-level evidence is needed, and the filtering note says how to narrow results. However, there is no explicit statement of when to use get_answers versus alternatives or exclusions, so the guidance is implied rather than stated.

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