related_queries

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly explains the return format (a dict with 'top' and 'rising' keys, each containing records with 'query' and 'value'), and crucially elaborates on the 'rising' value, including the important caveat that a value of 5000% represents a 'Breakout' marker, not an actual 5000% increase. This is valuable context beyond a basic summary. It doesn't cover error handling or side effects, but as a read-only operation it adequately explains expected behavior.

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 well-structured: a one-sentence summary, followed by an Args section and a Returns section. Every sentence adds value, and the critical note about the 5000% Breakout marker is front-loaded in the Returns section. There is no redundancy or filler, making it concise and easy to parse.

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 tool with 3 parameters and no output schema, the description covers both parameter semantics and return structure in detail. It lacks explicit usage guidance (covered in dimension 2) and doesn't mention potential pitfalls like geo-specific limitations or timeframe formats beyond the example, but it gives enough for an agent to call it correctly. The inclusion of the Breakout marker is a nice completeness touch. It earns a 4 because it doesn't explicitly address how this relates to sibling tools.

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 0%, so the description must explain all parameters, and it does so thoroughly. Each parameter is described with its type and purpose: 'keyword: a single search term,' 'timeframe: pytrends timeframe string, e.g. "today 12-m",' and 'geo: ISO country code (e.g. "IN"), or "" for worldwide.' It gives examples and edge cases (empty string for worldwide), which is exactly what an agent needs.

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?

The description states a clear purpose: 'Top and rising related search queries for a single keyword.' It specifies the verb (retrieve/list), the resource (related search queries), and the scope (for a keyword). It differentiates from sibling tools by focusing on queries rather than topics, though it doesn't explicitly name an alternative. The purpose is clear enough for an agent to understand what it does.

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 description provides no guidance on when to use this tool versus its siblings like related_topics or interest_over_time. It doesn't state conditions such as 'use when you need related queries, not topics.' The only hint is the phrase 'related search queries,' which implies a distinction, but there is no explicit 'when to use' or 'when not to use' guidance. This is a noticeable gap.

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