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

Adds context beyond annotations: data sources (SEC EDGAR/XBRL, FAERS, FDA), fiscal year handling, sorting by primary metric, paired data with citation URIs. Annotations already declare readOnlyOpenWorld, idempotent, non-destructive; no contradiction.

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?

Despite length, every sentence adds value: triggers, core function, alternative preference, per-type details, and output format. Front-loaded with usage signals and structured logically, making it dense yet accessible.

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, description explains return format (paired data, citation URIs, sorted by metric), covers edge cases (off-calendar fiscal years), and sets expectations for scale (replaces 8–15 lookups). Complete for an agent to know what to expect.

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 covers 100% of params, and description enriches meaning by detailing what each enum value does and giving examples for values. It also explains constraints and behavior (parallel call, sorting), adding value beyond schema descriptions.

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?

Description clearly states the tool performs side-by-side comparison of 2–5 companies/drugs in one parallel call, with explicit trigger phrases and per-type data sources. It distinguishes from sequential single-pack lookups, aligning with alternatives 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?

Explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', giving strong when-to-use guidance. Also clarifies the two entity types and what each returns, which helps the agent choose this tool over single-entity lookups.

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