related_topics

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

With no annotations, the description carries the full burden. It discloses the exact return structure (dict with 'top' and 'rising' lists) and explains the 'value' field semantics (0-100 relative interest, and the crucial 5000% breakout marker). It also clarifies that topics are grouped into clusters, not raw queries. It does not cover error cases or rate limits, so a small gap remains.

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 with Args and Returns sections, front-loading the main purpose. It is moderately sized but every sentence adds value, especially the breakout marker caveat. It could be slightly more concise, but the extra detail is important for correct interpretation of results.

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?

Given the lack of an output schema, the description provides a complete picture of the return value, including field names and value interpretations. All parameters are covered, and the key caveat about 5000% is included. Missing minor details like potential error codes, but for a read-only data retrieval tool, it is sufficiently complete for correct usage.

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 coverage is 0%, but the description fully explains all three parameters: keyword, timeframe (with example format), and geo (with example codes including worldwide). This completely compensates for the lack of schema descriptions, giving the agent everything needed to construct valid arguments.

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 clearly states the resource (related topics), the operation (top and rising), and the scope (for a single keyword). It explicitly differentiates from raw query strings, which is a key distinction from the sibling tool related_queries. This makes the purpose unambiguous and easily distinguishable.

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 gives clear context for when to use it ('for a single keyword') and provides example parameter values (timeframe, geo). It implies the difference from related_queries by noting 'Google's topic clusters, not raw query strings', but does not explicitly name alternatives or state conditions for choosing this over siblings. Still, the usage is well implied.

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