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

Annotations already declare readOnlyHint/idempotentHint/destructiveHint, but the description goes far beyond by revealing data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, sorting by primary metric, and the return of citation URIs. This provides crucial behavioral context that annotations do not convey.

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 a single dense paragraph but every sentence adds a distinct, valuable piece of information: trigger phrases, preference guidance, data sources, sorting behavior, and output format. It is front-loaded with the most important usage cues and avoids redundant wording.

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 (two entity types, 2–5 items, no output schema), the description fully covers return values (paired data + citation URIs), sorting logic, data sources, and edge cases like off-calendar fiscal years. It leaves no major questions for an agent choosing or invoking the tool.

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?

Though schema coverage is 100%, the description adds significant meaning to the 'type' parameter by explaining exactly what each type pulls (10-K financials for companies, FAERS/trial counts for drugs) and what the 'values' parameter should contain for each type. This goes well 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 opens with a clear verb phrase ('side-by-side comparison of 2–5 companies or drugs in ONE parallel call') and provides explicit trigger examples ('Compare X and Y'), making the tool's purpose unmistakable. It also distinguishes this from single-entity lookups by emphasizing the parallel comparison capability.

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 when to use this tool ('ALWAYS PREFER over sequential single-pack lookups when comparing entities') and lists specific user-language cues ('X vs Y', 'which is bigger', 'rank these companies'). This gives clear, actionable guidance for an AI agent deciding between this and alternative tools.

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