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

Beyond the read-only/idempotent annotations, the description reveals data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, sorting by primary metric, and return format (paired data + citation URIs). This is rich behavioral context that helps the agent set expectations.

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 longer than necessary with repeated phrasings like 'X vs Y' examples, but it front-loads the core purpose and all sentences carry substantive information. Minor redundancy prevents a 5.

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 covers purpose, usage, data sources, performance expectations (replaces 8–15 lookups), sorting behavior, and return format. This is sufficiently 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?

Although schema coverage is 100%, the description adds meaningful semantics: it explains what each type pulls (10-K financials for companies, adverse-event/trial counts for drugs) and gives concrete value examples (tickers/CIKs, drug names). This exceeds the bare schema definitions.

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 the tool performs side-by-side comparisons of 2–5 companies or drugs in a single parallel call, with specific verbs like 'compare' and a precise scope. It distinguishes itself from sibling tools like entity_profile by focusing on multi-entity comparison and batch efficiency.

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?

It explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and provides type-specific guidance (company vs drug). This gives clear when-to-use instructions and indirectly signals when not to use it (single entity lookups, >5 entities).

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