Top & related queries

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

Beyond the annotations (readOnlyHint, idempotentHint), the description adds crucial behavioral details: the 30-day time window, the fact that counts can arrive as strings (int64), and the optional parameters for narrowing results. This enriches agent understanding beyond safe-operation flags.

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?

Two sentences front-load the core functionality and behavior, with no unnecessary words. Every sentence adds value, making it highly concise and well-structured for quick agent comprehension.

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 complexity (4 parameters, no output schema), the description explains the two result types, data type nuance for counts, and parameter effects. It could mention result ordering or error handling, but overall covers essential context for effective use.

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 100%, so the description does not need to compensate. It provides an example for 'phrase' and reiterates parameter roles, but adds little new meaning beyond what's already in the schema's descriptions. Baseline score of 3 is appropriate.

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 it returns search-demand for a phrase over the last 30 days, specifying two distinct result types (containing phrases and related queries) plus totalCount. It distinguishes itself from sibling tools like 'list_regions' and 'raw_request' by focusing on keyword discovery and demand gauging.

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 explicitly says 'Use it to discover keywords and gauge demand,' providing a clear context of use. However, it does not mention when to avoid using it or suggest alternatives among siblings, such as when dynamic data is needed instead.

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