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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds useful behavioral context: pulls latest 10-K data, handles off-calendar fiscal years, sorts by primary metric, returns 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?

Description is front-loaded with example queries and clear directive. Somewhat verbose in detailing each entity type, but every sentence adds value. Could be slightly more concise.

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 the complexity (two entity types, multiple data sources), the description covers what the tool does, what data it returns, and how results are sorted. No output schema, but return format is described sufficiently (paired data + citation URIs).

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%. Description adds meaning beyond enum values and descriptions: for 'company' it specifies revenue, net income, cash, debt; for 'drug' it specifies adverse-event counts, FDA approvals, trial counts. Also explains sorting behavior.

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?

Description states it performs side-by-side comparison of 2-5 companies or drugs, specifying data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/trials for drugs). Clearly distinguishes from sibling tools like entity_profile (single entity lookup) by emphasizing parallel comparisons 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?

Explicitly says to prefer over sequential single-pack lookups and gives example queries. Does not provide explicit when-not-to-use scenarios, but context is clear that it's for multi-entity comparisons.

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