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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), but the description goes well beyond them: tier-based availability limits, that free/guest quarterly requests return a pro_unlocks note rather than an error, that null quarterly comes with a quarterly_note, that missing line items mean 'not reported' not 'fetch error', and that values are in financial_currency. This is unusually rich behavioral disclosure.

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-loads the most important distinction (raw data vs derived scores) and uses a labeled Tiers block that is easy to scan. It is somewhat long and repeats tier/period rules already in the schema, costing it a point, but no sentence is purely filler.

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?

Despite an output schema existing, the description covers every edge case an agent would need: coverage variance by sector, null/absent statement states, non-equity symbols, and tier gating. Nothing required to call or interpret the result 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%, and the description's tier/period/statement detail largely restates what the schema already documents (the period tier behavior appears near-verbatim in both). It adds the response shape {line_item: {period: value}} and currency semantics, but those are return-value concerns rather than parameter meaning, so the baseline 3 is appropriate.

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 resource (raw balance sheet / income statement / cash flow line items) and explicitly distinguishes itself from the sibling get_stock by naming its forensic_scores block as the alternative for derived metrics. An agent can tell exactly what this returns 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?

Gives an explicit when-to-use-this vs when-to-use-that routing rule: use this for raw statement data, use get_stock().forensic_scores for Altman Z / Piotroski F / Beneish M. It also frames the caller's intent (compute own trends/deltas/CAGRs) which clarifies the use case.

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