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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds valuable context beyond these: it specifies data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/trials for drugs), mentions correct handling of off-calendar fiscal years, explains result sorting by primary metric, and notes return of paired data with citation URIs. This gives a clear picture of behavior, though it could mention data freshness or potential limitations.

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 dense but every sentence adds value: example queries, usage preference, data source details, sorting logic, and return format. It is front-loaded with trigger phrases and avoids redundancy. The length is justified by the tool's complexity and two entity types.

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 absence of an output schema, the description sufficiently explains return values (paired data + citation URIs) and result ordering. It covers both entity types comprehensively and includes enough context for an agent to select and invoke the tool correctly, even without additional documentation.

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?

Although the schema already describes both parameters, the description significantly enriches their meaning. For 'type', it details exactly which financial metrics are pulled for companies and which counts for drugs. For 'values', it provides format examples (tickers/CIKs vs drug names) and clarifies the 2–5 count. This goes well beyond the schema's 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 defines the tool as a side-by-side comparison of 2–5 companies or drugs in one parallel call, using explicit trigger phrases like 'X vs Y' and 'which is bigger.' It distinguishes itself from sequential single-pack lookups by stating it should be preferred for comparisons, making the purpose unmistakable.

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 clear when-to-use guidance. It also specifies the two entity types and their data sources, and lists example queries that should trigger the tool, effectively differentiating it from alternative approaches.

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