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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, but the description adds substantial behavioral context: handling of off-calendar fiscal years, data sourcing from SEC EDGAR/XBRL and FAERS, result sorting by primary metric, and the return of paired data with citation URIs. This exceeds the safety-only disclosure and fully informs the agent about side effects and output characteristics.

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 dense but every sentence earns its place. It opens with trigger phrases, then states the tool's advantage, then details per-type behavior and output format, all without redundancy. The structure is effective for quick parsing by an agent.

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 types and no output schema, the description is highly complete. It explains data sources, specific metrics returned, sorting behavior, output format (paired data + citation URIs), and the efficiency benefit over sequential lookups. This gives the agent all needed context to invoke correctly and anticipate results.

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?

Although schema coverage is 100%, the description enriches both parameters: it explains the enum values (company/drug) with what each pulls, and gives concrete formats for values (tickers/CIKs vs drug names) with examples. It also explains that results are sorted by the primary metric, adding meaning beyond the raw schema 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 clearly states the tool's purpose: side-by-side comparison of 2–5 companies or drugs in one call, with specific trigger phrases ('compare', 'vs', 'which is bigger'). It distinguishes itself from sibling single-entity tools like entity_profile or query_entity by emphasizing the parallel comparison and its efficiency advantage.

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?

Provides explicit when-to-use guidance with trigger phrase examples and a clear directive: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also differentiates the two entity types (company vs drug) and specifies the data sources per type, giving the agent clear selection criteria.

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