related_topics

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

With no annotations provided, the description carries the behavioral burden. It discloses a non-obvious trait—that Google often returns no data—and explains the availability flag semantics. This goes beyond a basic summary, though it does not detail all response behavior or edge cases.

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 two concise sentences with the core purpose front-loaded and the important fallback caveat placed second. Every word earns its place.

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?

The description is useful for basic invocation and fallback decisions, and the output schema covers return values. However, with six parameters and zero schema descriptions, the lack of parameter guidance leaves meaningful gaps for correct invocation with non-default options.

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 compensate for the six parameters, but it only mentions 'keyword.' The meanings of geo, gprop, limit, category, and timeframe are left entirely to inference from their names and defaults.

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 that the tool returns 'Top and rising Knowledge-Graph topics related to a keyword,' identifying both the specific verb and resource. It also differentiates from the sibling tool related_queries by mentioning the fallback path, so an agent can distinguish them.

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 explicit guidance: Google often returns nothing here, and if available=false the agent should fall back to related_queries. This directly tells the agent when to use the tool and when to switch to an alternative.

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