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

Even with annotations (readOnlyHint, openWorldHint, idempotentHint) already covering safety, the description adds substantial behavioral context: it pulls specific financial metrics from SEC EDGAR/XBRL for companies and FAERS/FDA/trial data for drugs, handles off-calendar fiscal years correctly, sorts by primary metric so 'largest' reads off the top, and returns paired data with citation URIs. This far exceeds the structured annotations.

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 longer than average, but every segment earns its place: trigger phrases, explicit preference rule, per-type data details, sorting behavior, and return format are all packed in. It is front-loaded with the most important usage cues and uses formatting (quotes, em-dashes) for readability. It could be slightly tightened, but the density of useful information justifies the length.

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?

With only 2 params, no output schema, and clear annotations, the description fully explains what the tool does, how it behaves, what data it returns (paired data + citation URIs), and why it should be preferred. It even anticipates edge cases like off-calendar fiscal years. For a tool of this complexity, the description is remarkably complete.

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 both parameters with descriptions (100% coverage), so a baseline of 3 is warranted. However, the description adds significant semantic value by explaining what each type actually retrieves (e.g., 'company' → latest 10-K revenue + net income + cash + long-term debt; 'drug' → FAERS counts + FDA approvals + trial counts), and by giving concrete examples for values (tickers/CIKs vs drug names). This goes beyond the schema's minimal 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 opens with concrete query examples ('Compare X and Y', 'X vs Y', 'rank these companies') and states the core function: side-by-side comparison of 2–5 companies or drugs in a single parallel call. It distinguishes itself from sequential lookups ('Replaces 8–15 sequential lookups') and from sibling tools like entity_profile by explicitly focusing on multi-entity comparison.

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 says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', giving direct usage guidance. It also lists example queries that should trigger this tool, implicitly covering when to use it. No exclusion criteria are needed because the examples plus the 'ALWAYS PREFER' instruction clearly delimit the intended use case.

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