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

The description adds significant behavioral context beyond the annotations: it sources from SEC EDGAR/XBRL for companies (handling off-calendar fiscal years) and FAERS for drugs, sorts results by primary metric, and returns citation URIs. Annotations already indicate safe read operations; description enriches with concrete data sources and handling.

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 slightly long but every sentence adds value. It is front-loaded with example queries and structured into logical sections (usage, data details). No redundant information; achieves a good balance between completeness and conciseness.

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 tool has 2 parameters and no output schema, the description fully covers the input semantics, return value (paired data + citation URIs), sorting behavior, and data sources. It provides all necessary information for an agent to correctly invoke and interpret results.

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. The description adds meaningful context: explains that for company type, values should be tickers/CIKs; for drug type, names. It also clarifies the number range and gives concrete examples, slightly exceeding what the schema 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 explicitly states that the tool performs side-by-side comparison of 2-5 companies or drugs, with example queries like 'X vs Y' and 'rank these companies'. It clearly differentiates from sibling tools like entity_profile by emphasizing preference over sequential single 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 provides explicit context for when to use the tool (comparing entities, ranking) with example queries and states 'ALWAYS PREFER over sequential single-pack lookups', offering clear guidance on when not to use alternatives.

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