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

The description goes far beyond the annotations by disclosing what data is pulled for each type (10-K revenue/net income/cash/debt for companies; FAERS counts, FDA approvals, trials for drugs), correct handling of off-calendar fiscal years, sorting by primary metric, and the inclusion of pipeworx:// citation URIs. These behaviors are not present in the annotations, adding substantial value.

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 with valuable information: trigger phrases, use cases, data sources, fiscal-year handling, sorting, and output format. While it is long, every clause contributes practical guidance, and the most critical info (comparison action, preference over sequential lookups) is front-loaded.

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 specifying that it returns paired data with per-entity citation URIs and sorted by primary metric. It also conveys the parallel-call efficiency and the input constraints (2–5 entities). Despite its complexity, it provides a complete enough picture for an agent to select and invoke the tool correctly.

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 100% with descriptions and examples. The description enriches semantics by explaining that type='company' triggers SEC financial data extraction and type='drug' triggers adverse-event/trial data, and by detailing the expected data fields for each. This goes beyond the structural schema to help the agent understand the meaning of the parameter values.

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 function: side-by-side comparison of 2–5 companies or drugs in one parallel call. It uses a specific verb (compare) and resource (entities), and distinguishes itself from single-entity lookups by emphasizing "ALWAYS PREFER over sequential single-pack 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?

The description provides explicit when-to-use guidance, including trigger phrases like "which is bigger / better" and "rank these companies," and explicitly says to always prefer it over sequential lookups when comparing entities. It also contrasts with the alternative (8–15 sequential lookups), making the decision context clear.

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