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

The description adds substantial behavioral detail beyond the annotations: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), handling of off-calendar fiscal years, sorting by primary metric, and the return format (paired data + citation URIs). It also discloses that results are sorted so 'largest' reads off the top, which helps the agent set 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 one-sentence ideal, but every clause adds functional information (triggers, data sources, sorting, citations). It is front-loaded with the most critical trigger examples and preference instruction, then dives into specifics. The density justifies the length, though it could be slightly tightened.

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 compensates by describing the return format (paired data + citation URIs) and sorting behavior. It also covers data source specifics and fiscal year handling. Combined with read-only and idempotent annotations, the agent has a complete picture of what to expect and how to use the tool.

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?

While the schema already describes both parameters (coverage 100%), the description enriches their semantics by explaining what each 'type' actually pulls (e.g., company -> 10-K revenue, drug -> FAERS counts) and gives examples for 'values'. This goes beyond the bare enum and array descriptions, adding meaningful context for parameter selection.

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 trigger phrases and a specific verb+resource: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It clearly distinguishes itself from sibling tools like entity_profile or resolve_entity by emphasizing the multi-entity comparison scope and the efficiency gain over sequential lookups.

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?

Explicit guidance is given: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also enumerates the exact user expressions that should invoke this tool ('X vs Y', 'which is bigger', 'rank these companies') and scopes the tool to 2–5 entities, which tells the agent when to choose it over alternatives.

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