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

Annotations already mark it as read-only and non-destructive, and the description adds substantial detail: data sources (SEC EDGAR, FAERS, FDA, clinical trials), handling of fiscal year quirks, sorting by primary metric, and output format (paired data + citation URIs). No contradictions.

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 a single, well-structured paragraph that front-loads common query patterns, then states the core function, data sources, and efficiency claim. Every sentence adds value, no redundancy.

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 no output schema, the description adequately explains what each type returns (specific financial metrics for companies, counts for drugs) and mentions sorting and citation URIs. Minor gap: exact output format structure not detailed, but sufficient for agent understanding.

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 coverage is 100%, but the description adds meaning by explaining what each enum value returns (financials vs. regulatory data) and providing concrete examples for the values array (tickers/CIKs for companies, drug names). This helps the agent form correct inputs.

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 does side-by-side comparisons of 2-5 companies or drugs, with natural language triggers and explicit differentiation from sequential lookups. It unambiguously distinguishes from siblings like entity_profile.

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 explicitly says to prefer this tool over sequential single-pack lookups for comparisons and provides example queries. While it doesn't explicitly state when not to use it (e.g., for single entities), the context is clear enough to guide usage.

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