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

It discloses data sources (SEC EDGAR/XBRL, FAERS, FDA), specific metrics per type, handling of off-calendar fiscal years, sorting by primary metric, and returns paired data with citation URIs. This goes well beyond the annotations (readOnly, idempotent) by explaining actual operational behavior and edge cases.

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 each sentence earns its place: trigger phrases, preference guidance, type-specific data, sorting behavior, and return format. Despite being longer than a minimal description, it is well-structured and front-loaded with the most critical usage patterns.

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 compensates by stating it returns paired data and citation URIs, and notes results are sorted. It covers entity count limits, data sources, fiscal-year handling, and replacement of many sequential lookups. This is complete 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?

Schema coverage is 100% and already describes type and values with examples. The description adds the specific data each type pulls (e.g., 10-K revenue for companies, FAERS counts for drugs) and clarifies ticker/CIK vs drug name formats, enriching the meaning beyond the schema, although the schema already covers the basics.

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 clearly states a side-by-side comparison of 2–5 companies or drugs in one parallel call, using explicit trigger phrases ('Compare X and Y', 'X vs Y'). It distinguishes itself from sequential single-pack lookups by emphasizing parallel execution, which sets it apart from sibling tools 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 provides concrete query examples ('which is bigger / better / larger / more profitable') and explicitly instructs 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' This gives clear when-to-use guidance and hints at alternatives without ambiguity.

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