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

The description discloses the cost ($0.03/call) and details the response format (top topics, rising topics, granularity). It is explicitly read-only in nature ('Returns', 'reports'), with no hidden side effects. This is fully transparent given no annotations are provided.

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: it opens with a one-sentence summary, then describes the output, cost, parameters, and response granularity in a logical order. It avoids unnecessary repetition and is information-dense without being verbose.

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 covers the tool's purpose, output characteristics, pricing, all parameter details (including constraints and defaults), and response timeframes. It gives an example invocation. No critical operational detail is missing for an agent to use 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?

Even though the schema only has a generic 'params' object, the description thoroughly documents the actual parameters: keyword, geo (with country code examples and supported count), and timeframe (with all allowed values and response granularity semantics). It also provides an example. This fully compensates for the lack of schema 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?

The description clearly states the tool's function: 'Google Trends related topics for a keyword.' It specifies the output (topics, named entities, scores, percentage growth), the geographic scope, and provides a concrete example. This is unambiguous and distinguishes it from generic trend tools.

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 includes a use case ('market research and content planning') and an example invocation, which guides when to employ the tool. However, it does not explicitly contrast with sibling tools like related_queries or interest_by_region, so the guidance is not as sharp as it could be.

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