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

Beyond the readOnlyHint/idempotentHint annotations, the description discloses data sources (SEC EDGAR/XBRL, FAERS, FDA), handling of off-calendar fiscal years (AAPL Sep, NVDA Jan), sorting behavior by primary metric, and the return format of paired data with citation URIs. This is rich behavioral context.

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 dense but not bloated; every sentence contributes specific value. It front-loads example phrases and key behavior (parallel call, data sources, sorting). Slightly longer than minimal, but the length is justified by the tool's complexity.

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 no output schema, the description adequately explains return format (paired data + citation URIs), sorting, and data sources. It also addresses edge cases like off-calendar fiscal years. For a tool handling 2–5 entities and two types, this is complete.

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 baseline is 3. The description adds meaningful semantics beyond the schema by explaining that type='company' pulls 10-K revenue/net income/cash/debt, while type='drug' pulls FAERS/adverse-event counts, and by providing concrete examples for the values array (e.g., tickers like AAPL, drug names like ozempic).

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 precisely defines the tool as a side-by-side comparison of 2–5 companies or drugs in one parallel call, with concrete example queries like 'X vs Y' and 'rank these companies.' It clearly distinguishes from sibling tools by emphasizing the parallel call design versus sequential lookups.

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?

Explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing a strong when-to-use directive. It also differentiates by entity type (company vs drug), describing the specific data each type pulls, which guides tool selection.

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