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

Beyond annotations (readOnly, idempotent, openWorld), the description discloses specific data sources (SEC EDGAR/XBRL, FAERS, FDA), handling of off-calendar fiscal years, result sorting by primary metric, and return format with citation URIs. This rich behavioral context helps the agent anticipate outputs and side effects.

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 adds value: triggers, usage preference, type-specific details, output format, and efficiency gain. While longer than average, the complexity of the tool (two entity types, multiple data sources) justifies the length, and it is front-loaded with the most critical information.

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?

Given no output schema, the description compensates by explaining the return format (paired data + citation URIs) and result sorting. It covers both entity types, constraints (2–5 items), and the tool's advantage, making it complete for selection and invocation without additional context.

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 both parameters with enum and array descriptions (100% coverage). The description adds meaning beyond the schema by explaining what each entity type pulls (financial metrics for companies, adverse-event and trial counts for drugs), giving examples, and noting sort behavior. This enriches parameter understanding well above baseline.

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 performs side-by-side comparison of 2–5 companies or drugs in one parallel call. It specifies the resource (entities) and the action (compare), and distinguishes itself from sequential single-pack lookups and sibling tools 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?

Provides explicit trigger phrases ("X vs Y", "which is bigger", "rank these companies") and a strong directive to prefer this tool over sequential lookups when comparing entities. This gives the agent clear when-to-use guidance and implicitly notes when not to use it (for non-comparison lookups).

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