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

Annotations declare read-only, idempotent, open-world hints, but the description adds substantive behavioral details: pulls latest 10-K data from SEC EDGAR/XBRL, handles off-calendar fiscal years correctly (AAPL, NVDA examples), sorts results by primary metric, and returns paired data with citation URIs. These go well beyond the annotations and help the agent set expectations.

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?

Though moderately long, every sentence adds value—query examples, type-specific data sources, fiscal year nuance, sorting behavior, and output format. The structure starts with user-intent phrases, then proceeds to technical details, making it highly scannable and information-dense 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?

Given there is no output schema, the description compensates by stating the return includes paired data and pipeworx:// citation URIs, covers both entity types, and mentions sorting by primary metric. It addresses complexity, data sources, fiscal year edge cases, and the efficiency benefit, making it a complete standalone reference.

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%, and the description still adds meaningful semantics beyond the schema. It explains that type='company' pulls financial metrics and type='drug' pulls adverse-event/trial counts, and clarifies that values are tickers/CIKs for companies and drug names for drugs. This contextualizes the enum and array values effectively.

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 performs side-by-side comparisons of 2-5 companies or drugs in a single parallel call, with explicit example queries. It distinguishes itself from sibling tools like entity_profile by emphasizing multi-entity comparison and specific data sources (SEC EDGAR/XBRL, FAERS). The verb 'compare' plus resource types makes the purpose unmistakable.

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 usage guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and lists query patterns that trigger this tool. It also clarifies when to use company vs drug type and mentions it replaces 8-15 sequential lookups, giving clear alternatives and context.

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