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

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds substantial context beyond that: data sources (SEC EDGAR/XBRL, FAERS, FDA), specific metrics pulled, off-calendar fiscal year handling, sorting by primary metric, and citation URI return behavior. This gives the agent a clear model of what happens during execution.

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 information-dense but every sentence earns its place: trigger phrases, usage preference, type-specific behavior, sorting rule, and return payload. It is front-loaded with the most decision-critical information and remains scannable despite its length.

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 adequately explains what the tool returns ('paired data + pipeworx:// citation URIs per entity') and covers the key operational details. It is complete for an agent to select and invoke correctly, covering purpose, params, behavior, and 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 coverage is 100%, but the description adds meaning well beyond the schema by explaining what each 'type' value retrieves (company vs. drug) and what the 'values' array should contain. It also clarifies that results are sorted by the primary metric, which aids correct agent interpretation of the output.

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 trigger phrases and clearly states the tool performs side-by-side comparisons of 2–5 companies or drugs in a single parallel call. It distinguishes itself from sequential lookups and sibling tools like entity_profile by emphasizing the parallel aggregation and comparison-specific behavior.

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 instructs to 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', giving both a clear when-to-use and a direct comparison against the alternative. The examples of user phrasing ('which is bigger', 'rank these companies') further clarify the intended invocation context.

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