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

Beyond the readOnlyHint/openWorldHint annotations, the description explains that a topic aggregates spellings and translations, and quantifies the difference (AI topic scores 62 vs literal string's 1). This provides significant behavioral context for search volume semantics.

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 total. The first sentence states purpose and output, the second provides an illustrative example and integration context. Every sentence earns its place; there is no redundant or filler text.

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?

With one parameter and no output schema, the description fully explains what the tool returns (mid and type), why it matters (aggregated volume), and how it fits into the tool ecosystem. It is complete for a simple lookup tool.

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 schema already covers the single parameter (query can be a search term or an existing topic id) with 100% coverage. The description adds examples of entity types (company, product, person, etc.) but does not materially change understanding of the parameter's meaning or format beyond the schema.

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 uses a specific verb ('resolves') and names the resource ('Google Trends topic id') and output ('mid'), with an added 'type' field. It clearly distinguishes itself from sibling tools by focusing on entity resolution rather than trend measurement.

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 states that 'other TrendFlow tools accept a topic id anywhere they accept a keyword', implying this tool should be used to obtain topic ids for those tools. It gives a useful disambiguation example (Nike company vs goddess) but does not explicitly enumerate when not to use it or name alternative tools.

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