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

Annotations already cover read-only/idempotent/non-destructive, and the description adds substantial context beyond them: data provenance (8-K item 2.02, 6-K), which fields carry YoY/sequential comparisons, the unit/scale/null-scale convention, table_scale semantics, cross-share-class rows, and explicit coverage depth limits. This is exactly the behavioral disclosure an agent needs.

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?

Purpose is front-loaded in the first clause, then provenance, payload, unit caveats, and coverage limits follow in a single dense but non-redundant paragraph. Every sentence carries information an agent would otherwise have to guess.

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 an output schema present, the description need not explain return shapes, and it still supplies provenance, coverage depth, absence semantics and unit conventions. Nothing material for correct invocation 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?

Only one parameter (ticker) and schema description coverage is 100%, so the schema fully documents it with pattern and example. The description adds no ticker-specific semantics (e.g., class suffixes for share classes), so 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?

States a specific verb and resource ('Structured reads of a company's earnings reports') plus the added return of the next confirmed report date. An agent can immediately distinguish it from news/calendar siblings by the SEC-filing provenance and earnings-specific payload it names.

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?

Provides useful usage context – results start late July 2026, reports are quarterly, and 'an empty list is a normal answer, not an error' – but never names or compares against siblings that overlap (alphai_calendar for dates, alphai_ticker_news or alphai_article for earnings coverage). Usage is therefore implied rather than routed.

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