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

The description adds substantial behavioral context beyond the annotations: it reveals data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), specifies that off-calendar fiscal years are handled correctly, explains sorting by primary metric, and notes it returns paired data plus citation URIs. This enriches the read-only annotation with detail on what the tool actually does.

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 the calibration examples but every sentence contributes value: trigger phrases, usage preference, data sources, special fiscal-year handling, sorting, and return format. It is well-structured and front-loaded with usage cues, though it could be slightly trimmed without losing essential information.

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 two parameters and no output schema, the description gives adequate context: it explains the number of entities supported, the data sources, sorting behavior, and that it returns paired data with citation URIs. It does not mention error handling or invalid inputs, but the core usage is clear enough for an agent to 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?

While the schema provides full coverage of the two parameters (type and values), the description adds meaningful semantics: it explains that 'type=company' pulls financials from 10-K and 'type=drug' pulls adverse-event/trial counts, and it gives example values like tickers/CIKs or drug names. This goes beyond the schema's basic 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 comparisons of 2–5 companies or drugs in one call, using specific verbs like 'compare', 'rank', and 'head to head'. It also distinguishes from sibling tools by explicitly saying it should be preferred over sequential single-pack lookups, making its purpose unambiguous.

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 explicit when-to-use guidance with trigger phrases and states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also clarifies the two entity types (company vs drug) and what each pulls, which helps the agent decide which to use. No explicit exclusions for single-entity lookups, but the preference is clear.

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