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

The description adds substantial behavioral context beyond the annotations: it specifies the exact data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), explains that results are sorted by primary metric, mentions handling of off-calendar fiscal years, and notes the return of paired data with citation URIs. All these details enrich the agent's understanding without contradicting the read-only, idempotent, non-destructive 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 somewhat long but front-loaded with example phrases and clear purpose. Every sentence adds value, including technical details about data sources and fiscal year handling. However, it could be slightly trimmed without losing essential information, so it is not maximally concise.

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 the absence of an output schema, the description does an excellent job explaining the return format (paired data, citation URIs) and sorting behavior. It covers both entity types with sufficient detail, including edge cases like off-calendar fiscal years. The parameter count is small (2 required), and the description fully addresses what the agent needs to know to use 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 input schema has 100% coverage with descriptions for both parameters. However, the description goes further: it explains the meaning of the 'type' enum values ('company' or 'drug') and the expected format for 'values' (tickers/CIKs for companies, drug names for drugs). It also clarifies that the data returned depends on the type, providing semantic value beyond the raw schema.

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 states the tool's purpose: side-by-side comparison of 2-5 companies or drugs using specific financial or regulatory data. It provides example phrases like 'X vs Y' and 'rank these companies,' making the intent unmistakable. This distinguishes it from siblings like entity_profile or deep_research, which are for single entities or broader research.

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 explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing a strong usage directive. It gives examples of when to use the tool (comparisons, rankings, head-to-head) and implies that for single entity lookups, other tools should be used. The statement about replacing 8-15 sequential lookups further clarifies efficiency benefits.

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