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

Annotations already mark it read-only, open-world, idempotent, and non-destructive. The description adds value beyond annotations by detailing data sources (SEC XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and return of citation URIs. No contradiction.

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 front-loaded with trigger phrases and every sentence adds value. While slightly lengthy, it remains focused and efficient without redundancy.

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 return format (paired data + citation URIs), sorting behavior, and edge cases (off-calendar fiscal years). Provides sufficient context for correct invocation.

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% with descriptions for type (enum) and values (array constraints with examples). The description adds meaning beyond the schema by explaining what each type value entails (e.g., company pulls 10-K data) and how to format values (tickers/CIKs vs drug names), enhancing usability.

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 uses specific trigger phrases (e.g., "compare X and Y", "X vs Y") and states it does side-by-side comparison of 2-5 companies or drugs. It clearly distinguishes from sibling tools like entity_profile (likely single-entity) and deep_research (broader scope).

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 says "ALWAYS PREFER over sequential single-pack lookups when comparing entities," giving clear when-to-use advice. However, it does not explicitly state when not to use or mention alternatives, though sibling context implies them.

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