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

The description goes beyond the annotations by detailing exactly what data is pulled for each type: company (latest 10-K revenue, net income, cash, long-term debt from SEC EDGAR/XBRL) and drug (FAERS adverse-event counts, FDA approval counts, active trial counts). It also discloses fiscal-year handling, result sorting by primary metric, output format (paired data + pipeworx:// citation URIs), and performance impact (replaces 8–15 lookups).

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 clause earns its place. It front-loads trigger phrases, then explains behavior in a logical flow: what it does, when to prefer it, data specifics per type, sorting, output, and efficiency gain. No filler or repetition.

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 only two parameters and no output schema, this description is remarkably complete. It covers what the tool does, when to use it, what data each type returns, how results are ordered, and what the output contains (paired data + citation URIs). The performance benefit (replacing 8–15 lookups) adds useful context for agent decision-making.

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 the schema already covers parameter documentation (100% coverage), the description adds meaning by explaining what each type triggers: "type='company' pulls LATEST 10-K..." and "type='drug' pulls FAERS..." It also gives concrete examples for the values array (["AAPL","MSFT"], ["ozempic","mounjaro"]), which clarifies expected input format beyond the schema's generic 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?

The description opens with concrete trigger phrases ("Compare X and Y", "X vs Y", "head to head") and states the core action: "side-by-side comparison of 2–5 companies or drugs in ONE parallel call." It clearly distinguishes itself from sequential lookups and sibling tools like entity_profile by emphasizing the comparative, batched nature.

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?

Explicit guidance is provided: "ALWAYS PREFER over sequential single-pack lookups when comparing entities." It also includes example user intents ("which is bigger / better", "rank these companies") to signal when this tool should be selected, making the usage context unmistakable.

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