get_target_queries

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

No annotations are provided, so the description carries the behavioral burden. It does disclose the shape of the returned data via representative example queries, and 'Returns the list' implies a read-only listing, but it says nothing about static vs. live data, completeness, ordering, or authentication needs.

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 purpose in the first sentence, followed by supporting context and usage, so an agent can stop reading early. The six inline example queries are slightly heavy but they are scannable and directly illustrate the return content.

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-parameter, no-output-schema listing tool, the description supplies the essential missing context: what the list represents and what the entries look like. It stops short of explaining how the queries relate to sibling data sources, but nothing critical is absent.

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?

The tool takes zero parameters, so the schema has nothing to document beyond an empty object and there is no parameter semantics for the description to compensate for. Baseline of 4 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 ('Returns the list of AI search queries') and scopes it to a named subject, so an agent immediately knows what comes back. It does not, however, distinguish itself from the similarly named sibling query_expertise, so an agent must infer the split.

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?

Provides concrete when-to-use contexts: planning content, schema markup, or verifying expertise coverage. There are no exclusions and no explicit mention of query_expertise as the alternative, but the intended scenarios are clear rather than implied.

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