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

Beyond the annotations (readOnly, idempotent, etc.), the description discloses concrete behaviors: pulls 10-K data from SEC EDGAR/XBRL for companies, handles off-calendar fiscal years, retrieves FAERS/FDA/trial counts for drugs, sorts by primary metric, and returns citation URIs. This is rich, useful context that annotations alone do not provide.

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 concise but information-dense, with every sentence contributing new value: trigger phrases, data sources, fiscal-year handling, sorting, and return format. The opening phrase immediately anchors the tool's purpose, and the length is justified by 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?

Given the tool's complexity (two entity types, multiple data sources, sorting, citations), the description covers all critical aspects: what it does, when to use it, what data it retrieves, how results are ordered, and what the output includes. No output schema exists, so the description carries the burden, and it does so thoroughly.

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 already covers both parameters, the description adds meaningful semantics: it explains what 'type' controls (company vs drug data sources) and what the 'values' array should contain for each type, with concrete examples. The schema_description_coverage is 100%, but the description goes beyond the schema by clarifying the data pulled and the sorting behavior.

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 explicitly contrasts with sequential single-pack lookups and includes trigger phrases ('X vs Y', 'which is bigger'), making it easy for an agent to recognize when to invoke it.

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 gives explicit when-to-use guidance via trigger examples and an 'ALWAYS PREFER' directive over sequential lookups. It also differentiates between company and drug modes, describing the data sources and metrics for each, so the agent knows exactly when this tool is the alternative to other tools.

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