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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds significant context: for company type it pulls latest 10-K data with correct fiscal year handling, for drug type it pulls adverse-event counts and trial data, and it returns paired data with 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?

The description is moderately long but well-structured. It opens with trigger phrases, then defines the core purpose, followed by detailed behavior for each entity type, and closes with output format. Every sentence adds value, though some rephrasing could reduce verbosity without losing clarity.

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 low parameter count (2), high schema coverage (100%), and no output schema, the description adequately covers the input requirements, expected output (paired data + citation URIs), and special behaviors (fiscal year handling, sorting by primary metric). It is complete for an agent to use this tool effectively.

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% with descriptions for both parameters. The description adds semantic value by explaining valid values (tickers/CIKs for company, drug names for drug), constraints (2–5 items, minItems/maxItems clarified), and how values map to entity types. While the schema already specifies enums for 'type', the description provides concrete usage examples and clarifies the difference between company and drug inputs.

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. It provides natural language triggers ('compare X and Y', 'which is bigger', etc.) and specifies the data pulled for each entity type (SEC EDGAR financials for companies, FAERS data for drugs). This differentiates it from siblings like entity_profile which likely handles single entities.

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 states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing a clear directive for when to use this tool over alternatives. It also explains the behavior for each type and that results are sorted by primary metric, helping the agent understand when to invoke it.

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