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

With no annotations, the description discloses return shape ('Returns cat ... and msg'), the inclusion of 'All categories', and the token cost. It does not explicitly state read-only behavior, but 'List' implies a safe read operation and the disclosed return fields provide adequate transparency for a simple taxonomy fetch.

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 three concise sentences with the main purpose front-loaded, followed by return details, usage guidance, and cost. Every sentence contributes value without redundancy.

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 zero-parameter list tool with an output schema, the description fully covers purpose, usage timing, return structure, and cost. It is complete and well-suited for agent decision-making.

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?

The tool has zero parameters, so the baseline is 4. The description adds useful context by explaining how the returned labels are used in other tools' 'category field', which is more meaningful than the empty schema alone.

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 the specific action: 'List all Google Trends category and subcategory labels you can pass to other Google Trends tools in the category field.' This clearly distinguishes it from sibling tools like interest_over_time or regions by focusing on the category taxonomy.

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?

Explicit guidance is given: 'Use this before interest-over-time or interest-by-region calls when filtering by category.' This names the exact alternative tools and the precondition, making it easy for an agent to know when to invoke this tool.

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