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

The description goes well beyond the annotations by disclosing specific data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/clinical trials for drugs), handling of off-calendar fiscal years, sorting by primary metric, and output containing paired data with citation URIs. This adds substantial context without contradicting the read-only/idempotent hints.

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 every sentence carries actionable information. It front-loads the core purpose and usage triggers, then efficiently covers data sources, parameter semantics, and output characteristics without redundancy. The structure helps an agent quickly grasp when and how to invoke the tool.

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?

For a tool with no output schema, the description explains what the user gets back (paired data + citation URIs), how results are ordered, and the distinction between company and drug data. It even notes that it replaces 8–15 sequential lookups, giving the agent a full picture of the tool's value and behavior.

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?

Although the schema provides descriptions for both parameters, the tool description enriches them significantly—it explains what data each 'type' pulls (e.g., latest 10-K revenue, net income, cash, long-term debt for companies; FAERS counts and trial counts for drugs) and gives concrete examples for the 'values' array. This goes beyond the schema's baseline.

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 identifies the tool as a side-by-side comparison of 2–5 companies or drugs in a single call, with specific verbs like 'compare', 'rank', and 'head to head'. It also distinguishes itself from sequential single-entity lookups, setting it apart from sibling tools like entity_profile and resolve_entity.

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 states when to use this tool: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', and provides example user phrasings that trigger comparison intent. It also gives context for the 'type' parameter (company vs drug) and clarifies that results are sorted for easy interpretation.

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