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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable behavioral context: it pulls specific data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), handles off-calendar fiscal years, sorts results by primary metric, and returns paired data with citation URIs. It also notes it replaces 8–15 sequential lookups, revealing performance characteristics.

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 slightly long but front-loaded with practical examples and every sentence contributes unique value. It avoids repetition and uses clear formatting with trigger phrases, type specifics, and result behavior.

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?

Despite having no output schema, the description explains return format (paired data + citation URIs) and sorting order. Combined with the rich parameter details and sibling differentiation, it gives the agent a complete picture of the tool's behavior, including data sources and edge-case handling (off-calendar fiscal years).

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?

The schema covers both parameters with descriptions (100% coverage), but the description enriches them: type='company' triggers pulling specific financial fields from SEC EDGAR, while type='drug' pulls adverse-event and trial counts. This adds meaning beyond the schema's basic enum and value 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 clearly states the tool performs side-by-side comparison of 2–5 companies or drugs in a single parallel call, with specific examples of trigger phrases. It distinguishes itself from siblings by noting it should be preferred over sequential single-pack lookups for comparisons.

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?

Explicit usage guidance is given: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also provides concrete query examples and defines the scope (2–5 entities), making it clear when to use this tool versus alternative approaches.

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