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

Annotations already indicate readOnly, idempotent, and openWorld traits. The description adds substantial context: data sources (SEC EDGAR/XBRL, FAERS, FDA approvals), off-calendar fiscal year handling, metric sorting behavior, and pipeworx:// citation URIs. It fully discloses data provenance and result organization.

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 longer than average but front-loaded with trigger phrases and every sentence carries useful information, from usage guidance to data source details and result format. A slight trim could make it more concise, but it remains efficient for the tool's complexity.

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?

With no output schema, the description thoroughly covers return format (paired data + citation URIs), data sources, edge cases (off-calendar fiscal years), and sorting behavior. It leaves little ambiguity about what the tool does and what it returns, especially combined with rich annotations.

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 schema already covers parameter descriptions 100%, but the description significantly enriches meaning by explaining type-specific data pulls, providing concrete examples (AAPL/MSFT; ozempic/mounjaro), and noting the parallel execution and 2–5 range. It adds practical semantics beyond the enum values.

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 comparison of 2–5 companies or drugs in one parallel call, with explicit trigger phrases like 'Compare X and Y' and 'X vs Y'. It distinguishes itself from sibling tools like entity_profile by focusing on multi-entity comparisons rather than single-entity profiles.

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?

Explicitly instructs to ALWAYS PREFER this tool over sequential single-pack lookups when comparing entities, and provides concrete query examples that should trigger its use. While no sibling tool names are mentioned, the when-to-use and when-not-to-use guidance is unambiguous.

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