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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, but the description adds substantial non-obvious behavioral context: data sources (SEC EDGAR/XBRL vs FAERS), fiscal-year edge cases (AAPL Sep, NVDA Jan), sorting by primary metric so 'largest' reads off the top, and the return format (paired data + citation URIs). This goes well beyond the structured annotations.

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 front-loaded with query examples and usage scope, then efficiently covers data sources, edge cases, sorting, and output format. Every sentence contributes, though the opening sentence is long and packs many examples, making it slightly less scannable than ideal.

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 only 2 parameters and no output schema, the description covers everything needed: supported entity types, count limits (2–5), data sources, fiscal-year handling, sorting behavior, return shape (paired data + URIs), and the performance advantage over sequential lookups. It is fully sufficient for an agent to select and invoke the tool correctly.

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?

The schema documents both parameters with descriptions, but the tool description enriches them significantly: it explains what each type means for data pulling (latest 10-K for companies, FAERS/approval/trials for drugs) and provides concrete examples for values ('AAPL','MSFT','ozempic','mounjaro'). This adds meaning beyond the schema's enum and array item descriptions.

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 opens with concrete query patterns ('Compare X and Y', 'X vs Y', 'which is bigger') and explicitly states a side-by-side comparison of 2–5 companies or drugs in one parallel call. It distinguishes itself by the 'ALWAYS PREFER over sequential single-pack lookups' note, clearly separating it from single-entity lookup siblings like entity_profile.

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 description explicitly enumerates when to use the tool (comparison queries like 'rank these companies' or 'head to head') and advises preferring it over sequential single-pack lookups. However, it does not name a specific alternative tool or state when NOT to use it (e.g., when only one entity is needed), so it stops short of full when/when-not specification.

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