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

Beyond the annotations (read-only, open-world, idempotent, non-destructive), the description discloses concrete behaviors: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), handling of off-calendar fiscal years, sorting by primary metric, and return format (paired data + citation URIs). It also notes the parallel-call optimization, adding significant value beyond the annotation hints.

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 information-dense without fluff. Each sentence earns its place: triggers, usage preference, per-type details, sorting behavior, return format, and efficiency claim. It is front-loaded with the core purpose and structured logically, making it easy to parse.

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 two modes of operation and no output schema, the description provides comprehensive context: what is compared, which metrics are pulled, how results are ordered, what output format looks like, and how fiscal years are handled. It also addresses the bounded input size (2–5). This is sufficient for an agent to select and invoke 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?

The schema already covers parameter basics (enum for type, array constraints for values) with 100% coverage. The description adds richer semantics by explaining what each type retrieves (e.g., 'LATEST 10-K revenue + net income + cash + long-term debt' for companies, 'FAERS adverse-event counts...' for drugs) and gives concrete examples. This goes beyond the schema but does not fully describe every edge case, so a 4 is appropriate.

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 uses a specific verb ('compare') and resource ('companies or drugs') and clearly scopes it to side-by-side comparison of 2–5 entities in one parallel call. It lists example natural-language triggers and explicitly contrasts with sequential single-pack lookups, which distinguishes 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?

The description explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and notes it replaces 8–15 sequential lookups. This provides a clear directive for when to use the tool and implicitly highlights why it's superior for comparison tasks, though it doesn't name specific alternative tools.

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