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

Annotations already cover the safety profile: readOnlyHint, idempotentHint, and destructiveHint are all aligned. The description adds meaningful behavioral context beyond those annotations by clarifying that results are cross-validated by Fodda's pipeline and that they connect analyst/CEO statements with consumer trend signals. It does not detail return structure or pagination, but the schema already documents the limit parameter and the annotations lower the burden.

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 two sentences with no filler: the first states what the tool returns, and the second explains the analytic connection that gives the output its meaning. It avoids repeating the title or restating schema fields, and every clause earns its place.

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 read-only, optional-parameter lookup with a fully documented schema and no output schema, this description supplies the key missing context: the domain (earnings reports), the validation process, and the analytic link between executive commentary and consumer trends. It could additionally describe the shape of a trend result or list exclusion criteria, but those gaps are minor given the overall simplicity of the 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?

Schema description coverage is 100%, so the baseline is 3; all five parameters already have explicit descriptions. The tool description adds general context around earnings reports and validated trends, which makes the search/sector/ticker filters feel coherent, but it does not add any parameter-specific semantics beyond what the schema already provides.

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 ('Returns') and names a distinct resource: market-validated consumer trends from corporate earnings reports, cross-validated by Fodda's analysis pipeline. It also clarifies the analytic output by connecting earnings commentary with consumer trend signals. It does not explicitly contrast with sibling tools like search_insights or get_evidence, but the 'validated trends' framing is specific enough to differentiate it.

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 intended use is implied: call this tool when you want pre-validated consumer trends derived from earnings-report commentary rather than raw evidence or general graph search. However, it never explicitly states when not to use it or names alternative tools, so an agent must infer the routing from the 'validated' and 'earnings' context.

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