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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds substantial context: pulls from SEC EDGAR/XBRL, handles off-calendar fiscal years, sorts by primary metric, and returns paired data with citation URIs. This goes well beyond the annotation baseline.

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 earns its place. It front-loads example queries and the core function, then adds data-source details and sorting behavior. It's appropriately sized for the tool's complexity and avoids fluff.

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 having no output schema, the description covers return values (paired data + citation URIs), data sources, sorting, and edge cases (off-calendar fiscal years). It also explains why the tool is efficient, replacing 8–15 sequential lookups. This gives a complete context for an agent to use the tool correctly.

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%, but the description enriches both parameters significantly. It explains what each 'type' value does, gives concrete examples for 'values', and clarifies behavioral details like fiscal-year handling and result sorting, adding meaning beyond the schema definitions.

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's purpose: side-by-side comparison of 2–5 companies or drugs in one parallel call. It includes specific query examples and explicitly distinguishes itself from sequential single-pack lookups, showing a strong differentiation from sibling tools.

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?

It provides explicit usage guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' This tells the agent when to use this tool and implicitly when not to (for single-entity lookups). The description also explains which data sources are used for each entity type.

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