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: data sources (SEC EDGAR/XBRL, FAERS), specific financial metrics, correct handling of off-calendar fiscal years, sorting by primary metric, and return of paired data with citation URIs. This far exceeds 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 long but well-structured: trigger phrases, core purpose, type-specific details, sorting, and output format. There is minor redundancy between 'ALWAYS PREFER over sequential single-pack lookups' and 'Replaces 8–15 sequential lookups,' but overall every sentence adds value for a complex tool.

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?

For a tool with no output schema, the description covers what data is pulled, how results are sorted, and that citations are returned. It could explicitly mention response units or exact formatting, but the provided detail is sufficient for an agent to know what to expect and how to invoke it 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?

With 100% schema coverage the baseline is 3, but the description adds critical meaning beyond the schema: it maps type=company to specific metrics (revenue, net income, cash, long-term debt) and type=drug to adverse-event/approval/trial counts, and explains fiscal-year handling that affects values. This gives agents a much richer understanding of parameter behavior.

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 natural-language trigger phrases and clearly states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It goes beyond a generic 'compare' verb by specifying the resource types (companies/drugs) and the parallel execution model, distinguishing it from sibling 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?

Explicitly states when to use the tool via query examples ('X vs Y', 'which is bigger', 'rank these companies') and gives an explicit directive to prefer it over sequential lookups. It also clarifies the 2–5 entity limit and type-specific use cases (company financials vs drug trial/adverse-event data).

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