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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds substantial context: data sources (SEC EDGAR/XBRL, FAERS), specific financial metrics and adverse-event counts, handling of off-calendar fiscal years (e.g., AAPL, NVDA), sorting by primary metric, and return format (paired data + citation URIs). This goes well beyond the annotations and enriches the agent's understanding.

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 moderately long but each sentence provides valuable information: trigger phrases, action, type-specific behavior, sorting, return format, and efficiency gains. It is front-loaded with usage examples and structured logically. While not as terse as some, every part earns its place for a complex tool.

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 tool's complexity, no output schema, and sibling tools like entity_profile, the description is complete: it specifies when to use, what data is returned, how results are ordered, and even handles domain-specific edge cases like off-calendar fiscal years. An agent can confidently select and invoke it correctly based solely on this description.

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 input schema has 100% coverage with descriptions for both parameters, including enums and examples. The description adds further value by explaining what data is pulled for each type ('company' -> 10-K figures; 'drug' -> FAERS/FDA/trial counts), but the core parameter semantics are already well-defined in the schema, so a 4 reflects the added depth without over-crediting.

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. It uses specific verbs like 'compare' and examples of user intents, and distinguishes itself from sequential single-pack lookups, making its purpose unambiguous and distinct from siblings.

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 explicitly instructs to 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and provides trigger phrases (e.g., 'X vs Y', 'rank these companies'). It also states it replaces 8-15 sequential lookups, giving clear guidance on when to use this tool versus alternatives.

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