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

Annotations already indicate read-only and idempotent behavior. The description adds rich detail: data sources (SEC EDGAR/XBRL, FAERS, FDA, trial counts), handling of off-calendar fiscal years, sorting by primary metric, and citation URIs. No contradictions with 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?

Every sentence adds value, covering triggers, behavior, data specifics, sorting, and efficiency. Slightly long but front-loaded with user-facing query examples. Could be slightly more structured with bullet points, but overall efficient.

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?

Given no output schema, the description adequately covers return format (paired data + citation URIs), data sources, sorting, and parameter nuances. The tool is well-characterized for an agent to select and invoke 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% but the description adds critical meaning: explains what 'company' and 'drug' types pull (specific financial metrics vs. adverse event/trial data), and clarifies that 'values' should be tickers/CIKs or drug names. This goes well beyond the schema 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 explicitly lists natural-language triggers like 'Compare X and Y', 'which is bigger', 'head to head', and clearly states it performs side-by-side comparison of 2–5 companies or drugs. It distinguishes itself from sibling tools like entity_profile by emphasizing efficiency over sequential lookups.

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 strong guidance: 'ALWAYS PREFER over sequential single-pack lookups' and gives concrete query examples. It does not, however, mention when not to use it (e.g., when needing more than 5 entities or different data types).

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