Stock Earnings Estimates & Results: EPS Forecast vs Actual

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true and idempotentHint=false, so the safety profile is carried by structured data. The description adds real value beyond that: it discloses the APIFY_TOKEN auth requirement, that the run executes against the caller's Apify account (billing implication), that maxRows drives cost, and that run/dataset IDs enable paging. The 'Read-only' sentence largely restates the annotation, but the auth, account, and cost disclosures justify above-baseline credit.

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?

Front-loaded with purpose, then clearly labelled When to use / Returns / Behavior / Docs sections, with no filler clauses. The title and tagline are duplicated at the start and the read-only remark is redundant with annotations, costing it the top band.

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 six optional parameters and no output schema, the description still covers everything an agent needs: what the rows contain, that results are per-row JSON with dataset IDs for paging, the auth/billing model, and the cost lever. Nothing required for a correct first call is missing.

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 mode, symbols, maxRows, minBeatRate, includeAnnual and onlyRaisedEstimates are already fully documented in the schema. The description only echoes the concepts (beat frequency, raised/cut forecasts) in prose without adding syntax, units, or edge-case behavior, so the baseline 3 applies.

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 specific verb and resource — analyst EPS estimates vs reported earnings for any US stock, sourced from NASDAQ — and enumerates the concrete content (next-quarter consensus, past actuals, beat/miss percentage, beat frequency, four-week estimate revisions). No sibling tool covers this data, and an agent can tell exactly what it gets without opening the schema.

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?

An explicit 'When to use' line states the triggering need (analyst EPS estimates vs reported earnings, beat/miss history, recent revisions). It gives clear positive guidance but names no exclusions or alternative tools, so it stops short of full routing guidance.

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