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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds substantial context: type-specific data sources (SEC EDGAR/XBRL, FAERS), fiscal-year handling, result sorting by primary metric, and return format with citation URIs. This goes well beyond annotations and clearly sets behavioral expectations.

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 the calibrated high example but remains front-loaded with trigger phrases and key guidance. Every sentence adds value, though the 'Replaces 8–15 sequential lookups' clause is somewhat redundant with the ALWAYS PREFER statement. It is dense but not padded.

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 no output schema, the description properly explains return values (paired data + citation URIs) and sorting behavior. It covers both entity types, data sources, and edge cases like off-calendar fiscal years, making it complete for a tool of moderate complexity.

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%, so the schema already documents type and values. The description adds meaningful beyond-schema details such as the 2–5 range, example tickers/drug names, and the fact that results are sorted by primary metric, which helps interpret values. However, it does not add much about parameter syntax beyond examples.

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 explicitly states the tool performs side-by-side comparisons of 2–5 companies or drugs in one call, using clear trigger phrases like 'X vs Y' and 'which is bigger.' It distinguishes itself from sequential single-entity lookups and sibling tools like entity_profile by 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 gives explicit when-to-use guidance with example queries and an 'ALWAYS PREFER' directive over sequential single-pack lookups. It also clarifies type-specific usage for company vs drug, but does not name specific alternative tools, which is a minor gap.

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