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

Annotations mark this as readOnlyHint true and destructiveHint false, and the description adds meaningful behavioral context beyond that: parallel fan-out across 80+ sources, categorized data blocks, source attribution, and metadata in the return. It does not mention latency, rate limits, or how to interpret empty results, but for a read-only open-world data lookup tool the added context is strong.

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 tight and front-loaded: it defines the tool, states when to use it, gives examples, and ends with the behavioral note about search_graph. Every sentence contributes useful information without redeploying or padding.

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 there is no output schema, the description adequately explains the return shape at a high level: categorized data blocks with source attribution and metadata. It is also sufficiently rich on conditions, sources, and call-order guidance. The main gap is that it does not describe possible failure modes, empty results, or how rich the output blocks actually are, but the tool is still reasonably contextual.

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 baseline is 3. The description itself does not add much param-level detail, though it does reinforce that query can be demand/attention-shaped or count/topic-oriented. The schema already covers country hints, brands, domain, and graph IDs thoroughly, so the description is not burdened to fill in param gaps.

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 names a concrete resource (supplemental market context) and a specific action (retrieve data from 80+ authoritative sources), and clearly frames when this tool is meant for: thin or empty curated coverage. It also contrasts with the search_graph workflow by explicitly positioning this as the follow-up layer, so an agent can distinguish it from sibling 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 gives an explicit trigger condition: call it when curated coverage is thin or empty, or when the query is demand/attention-shaped. It also provides concrete examples (search volume, economic series, census data) and names the preceding tool condition ('after search_graph indicates thin/empty coverage'), which gives clear operational guidance.

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