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

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds rich behavioral details: specific financial metrics pulled for companies (10-K revenue, net income, etc.), for drugs (FAERS counts, FDA approvals), handling of off-calendar fiscal years, sorting by primary metric, and return of paired data with citation URIs. No contradictions.

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 concise (5-6 sentences) and front-loaded with example queries and a strong usage directive. Every sentence adds value without redundancy, making it efficient for an agent to parse quickly.

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 no output schema, the description fully explains return format (paired data + citation URIs) and sorting behavior. It covers input parameters, data sources, and the parallel call nature. For a tool with 2 parameters, it provides sufficient context for correct invocation.

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. The description adds value by explaining the meaning of 'company' vs 'drug' types (data sources), providing concrete examples for the values parameter (tickers/CIKs or drug names), and noting sorting behavior. This enriches the semantic understanding beyond the schema.

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 it performs side-by-side comparison of 2-5 companies or drugs in one call, providing specific data sources (SEC EDGAR for companies, FAERS for drugs). It distinguishes from siblings by explicitly stating preference over sequential single-pack lookups, contrasting with tools like entity_profile.

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 usage guidance with example queries ('compare X and Y', 'X vs Y', etc.) and a strong directive to always prefer this over sequential lookups. It effectively tells the agent when to use the tool and gives alternatives.

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