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

Annotations already declare readOnly/idempotent/non-destructive. The description adds valuable behavioral details: company data from SEC EDGAR/XBRL with off-calendar fiscal year handling, drug data from FAERS/FDA, results sorted by primary metric, and return of paired data with citation URIs. This goes well beyond annotation information.

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 information-dense but slightly verbose with multiple quote examples and capitalized phrases. Each sentence adds value, and the structure front-loads the core purpose before diving into type-specific details. Minor redundancy in examples ('X vs Y' / 'X versus Y') prevents a perfect score.

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?

Despite having no output schema, the description fully informs the agent: what data is pulled per type, how results are sorted, what return format is (paired data + citation URIs), and why it's efficient (replaces 8–15 sequential lookups). Combined with the clear schema and annotations, the description is completely adequate 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?

Schema covers both parameters fully, but the description adds useful semantics: type values are explained with what metrics are pulled per type, and values clarifies tickers/CIKs for companies vs names for drugs. The extra context about fiscal year handling and sorting enriches 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 explicitly states the tool performs side-by-side comparison of 2–5 companies or drugs in one parallel call, with specific query patterns ('X vs Y', 'rank these companies'). It clearly distinguishes from sibling tools like entity_profile by stating 'ALWAYS PREFER over sequential single-pack lookups when comparing 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?

Provides explicit when-to-use guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and enumerates triggering query types. It also clarifies that type='company' vs type='drug' determines data sources, giving concrete context for selection.

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