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

While annotations already mark readOnlyHint=true and idempotentHint=true, the description adds substantial behavioral context: data sources (SEC EDGAR/XBRL, FAERS), correct handling of off-calendar fiscal years, sorting by primary metric, and the return format with citation URIs. This goes well beyond what annotations 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 dense but efficiently front-loaded with trigger phrases and the core action. Some redundancy exists between 'ALWAYS PREFER over sequential single-pack lookups' and 'Replaces 8–15 sequential lookups,' but overall the structure is logical and every section earns its place.

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 no output schema, this description is remarkably complete: it explains inputs, entity-type-specific behavior, data provenance, ordering, and return artifacts. The note about off-calendar fiscal years and the comparison efficiency claim add context that fully prepares an agent to invoke the tool correctly.

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 covers both parameters with 100% description coverage, including an enum for type and min/max constraints for values. The description adds meaning by mapping type to specific data pulled (10-K financials vs. FAERS counts/trials) and providing concrete examples like ['AAPL','MSFT'] and ['ozempic','mounjaro'], which enhances parameter understanding.

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 and states exactly what the tool does: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It clearly distinguishes itself from sequential single-pack lookups and sibling tools like entity_profile by emphasizing parallel comparison of multiple entities.

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 triggers ('X vs Y', 'which is bigger', 'rank these companies') and says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' However, it does not name a specific alternative tool (e.g., entity_profile), so the guidance stops short of fully explicit alternatives.

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