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

Even though annotations already declare readOnlyHint=true and idempotentHint=true, the description adds crucial behavioral context: it details data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), explains that off-calendar fiscal years are handled correctly, states that results are sorted by primary metric, and mentions the return format (paired data + 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 yet well-organized. It front-loads the core purpose and usage preference, then systematically covers data sources, metric details, sorting behavior, and return format. Every sentence adds distinct value, with no filler or repetition. 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 moderate complexity (two entity types, 2–5 values, no output schema), the description is impressively complete. It covers usage triggers, when to prefer it, what data is pulled for each type, how results are sorted, and what the return includes (paired data + citation URIs). Annotations cover safety (read-only, idempotent), and the description fills in all functional context needed for correct invocation.

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?

The schema already describes both parameters with 100% coverage, but the description adds significant semantic meaning: it explains what each type returns (financial metrics vs. adverse-event/trial counts), gives concrete examples for the values array (['AAPL','MSFT'], ['ozempic','mounjaro']), and clarifies that sorting makes 'largest' readable from the top. This goes well beyond the structured schema.

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 ONE parallel call.' It uses specific verbs and resources, and distinguishes from siblings by explicitly naming the alternative 'sequential single-pack lookups' which it replaces.

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: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also provides trigger phrases ('X vs Y', 'rank these companies') and notes that it replaces 8–15 sequential lookups, effectively stating the advantage over alternatives.

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