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

Annotations declare readOnly/openWorld/idempotent, but the description goes far beyond by detailing data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and return of paired data + citation URIs. This is substantial behavioral context beyond the 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 long but every sentence earns its place: usage triggers, preference guidance, per-type behavior, sorting, return format, and scope limits. It is front-loaded with user query patterns and maintains tight structure without 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?

Despite lacking an output schema, the description explains return format (paired data + citation URIs) and ordering behavior. It covers entity types, limits (2–5), data sources, and fiscal-year handling, making the tool fully self-contained for an agent to select and invoke it 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?

Schema coverage is 100%, so baseline is 3, but the description adds meaning by explaining that type='company' pulls financial data from SEC filings while type='drug' pulls FAERS/fda/trial counts. It also gives concrete examples for the values array, which enriches what the schema alone provides.

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 natural language user queries ('Compare X and Y' / 'X vs Y') and explicitly states the tool performs 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' This clearly identifies the verb (compare) and resource (entities), and distinguishes it from sequential lookups, meeting the highest bar.

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?

It explicitly instructs 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' naming the alternative and specifying the condition (when comparing entities). Examples of trigger phrases provide additional context, making it clear when this tool should be invoked.

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