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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context: it runs in one parallel call, handles off-calendar fiscal years (e.g., AAPL, NVDA), sorts results by primary metric, and returns paired data + citation URIs. It also mentions data sources (SEC EDGAR/XBRL, FAERS), enriching the agent's understanding beyond 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 fairly long but every sentence delivers critical information—trigger phrases, type-specific behaviors, sorting, and efficiency gains. It is front-loaded with usage examples and maintains a logical flow. Slightly dense but not bloated.

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 two entity types and an array parameter, the description fully explains what data is returned (paired data + citation URIs), how results are ordered, and why it replaces many sequential lookups. It compensates for the absence of an output schema, making the tool's behavior predictable and 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?

Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond the schema by detailing exactly what each type pulls: company type retrieves 10-K revenue, net income, cash, and long-term debt; drug type retrieves FAERS adverse-event counts, FDA approval counts, and active trial counts. This helps the agent understand parameter implications, meriting a 4.

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 uses the specific verb "compare" with a clear resource (companies or drugs), and states the scope (side-by-side, 2-5 entities in one parallel call). It explicitly differentiates from sequential single-pack lookups, making it distinct from sibling tools like entity_profile and resolve_entity.

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 lists exact user query patterns ("Compare X and Y", "which is bigger", "rank these companies") and explicitly states "ALWAYS PREFER over sequential single-pack lookups when comparing entities." It also distinguishes between company and drug use cases with type-specific data pulled, providing clear when-to-use guidance.

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