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

Annotations already declare the tool read-only, open-world, idempotent, and non-destructive. The description adds valuable behavioral context beyond these: it uses a parallel call pattern, handles off-calendar fiscal years correctly, sorts results by primary metric, and returns paired data with citation URIs. No contradiction with annotations.

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 well-organized: trigger phrases lead, followed by core value proposition, then type-specific details, and finally output format. Every sentence adds operational information with no filler, making it appropriately sized for the tool's complexity.

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?

Despite no output schema, the description covers inputs (types, value formats), outputs (paired data, citation URIs, sorted by primary metric), edge cases (off-calendar fiscal years), and usage context (parallel vs sequential). For a tool with 2 parameters and two distinct modes, this is fully complete.

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?

While the schema covers 100% of parameters, the description significantly enriches their meaning. It explains that type='company' pulls 10-K financials (revenue, net income, cash, long-term debt) from SEC EDGAR/XBRL, while type='drug' pulls FAERS adverse-event counts, FDA approval counts, and trial counts. It also clarifies value formats (tickers/CIKs vs drug names) and the sorting behavior tied to the primary metric.

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 purpose: side-by-side comparison of 2–5 companies or drugs in a single parallel call. It distinguishes itself from siblings by explicitly noting it should be preferred over sequential single-pack lookups, and includes trigger phrases ("X vs Y", "which is bigger") that make intent unmistakable.

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 with trigger phrase examples and a direct statement to prefer this tool over sequential lookups. It also details type-specific usage (company vs drug) and data sources, giving the agent clear context for when each variant is appropriate.

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