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

Annotations already declare readOnly/openWorld/idempotent hints. The description adds substantial context: specific data pulled for company (10-K revenue, net income, cash, long-term debt) and drug (FAERS counts, FDA approvals, trial counts), off-calendar fiscal year handling, sorting by primary metric, and citation URI returns. 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 but every sentence earns its place: usage triggers, preference instruction, data source details, fiscal year handling, sorting behavior, and return format. It is front-loaded with query examples and avoids filler.

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 two distinct modes (company/drug) and no output schema, the description covers key aspects: what data is pulled per mode, how results are ordered, and the return structure (paired data + citation URIs). This is sufficient for an agent to select and invoke the tool correctly, especially with sibling 'entity_profile' available for single lookups.

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?

Though schema coverage is 100%, the description enriches parameter semantics: it explains what each enum value ('company' vs 'drug') produces, gives example value formats (tickers/CIKs vs drug names), and clarifies the array count constraints (2–5). This goes far beyond the schema's basic type descriptions.

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 identifies the tool as a side-by-side comparison engine for 2–5 companies or drugs, using explicit query patterns like "X vs Y" and "which is larger." It distinguishes itself from single-entity lookups (sibling tool 'entity_profile') by emphasizing parallel comparison and replacing sequential lookups.

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 states explicitly when to use this tool: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also provides concrete example queries and contrasts with the alternative (sequential lookups), making the choice unambiguous.

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