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

The description discloses substantial behavioral details beyond annotations: it makes a parallel call rather than multiple calls, pulls from SEC EDGAR/XBRL with correct handling of off-calendar fiscal years, sorts results by primary metric, and returns citation URIs per entity. This equips the agent with expectations about performance, data sources, and output structure—far beyond the readOnly/idempotent hints.

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 dense but every sentence serves a purpose: usage triggers, tool preference rule, data sources, sort behavior, return format, and efficiency claim. It is front-loaded with the most important info (what it does) and wastes no words. Structure is scannable and actionable.

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?

For a tool with no output schema and two entity types, the description covers the essentials: scope (2–5 entities), data sources per type, handling of fiscal year edge cases, sorting by primary metric, and citation URIs. It also communicates the key benefit of replacing many lookups, giving the agent full context for 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?

Although the schema already documents both parameters (type enum and values array), the description adds valuable semantic context: type="company" maps to 10-K financials, type="drug" maps to FAERS/trial data, and values format examples are given (tickers for company, names for drug). This enriches the bare schema with real-world meaning.

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 concrete natural-language query patterns ("Compare X and Y", "which is bigger") and explicitly defines the action: side-by-side comparison of 2–5 companies or drugs in a single parallel call. It clearly distinguishes itself from sibling tools like entity_profile by framing sequential single-pack lookups as inferior for this use case.

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 states an explicit rule: "ALWAYS PREFER over sequential single-pack lookups when comparing entities." Type-specific behavior for company vs drug is explained, and the mention of "Replaces 8–15 sequential lookups" reinforces when this tool is appropriate. This gives the agent clear decision criteria.

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