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

The description goes well beyond the annotations by disclosing data sources (SEC EDGAR/XBRL, FAERS, FDA), handling of off-calendar fiscal years, sorting by primary metric, and return format including citation URIs. It also notes the tool replaces 8–15 sequential lookups, which is useful operational context. No contradiction with readOnlyHint or other 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 dense but every sentence contributes meaningful detail. It opens with user phrasing examples and ends with the efficiency claim. It could be slightly more structured (e.g., separating company vs. drug details), but it remains readable and focused 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?

Given no output schema, the description adequately covers return behavior: 'paired data + pipeworx:// citation URIs,' sorted results, and per-type metrics. It also covers limits (2–5 entities), data sources, and special handling of fiscal years. This is complete enough for an agent to know what to expect and when to invoke it.

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%, but the description adds significant meaning: it explains what each type retrieves (10-K metrics vs. adverse-event/trial counts), gives concrete examples for values, and clarifies sorting behavior. This goes well beyond the bare schema descriptions, enriching the agent's understanding of how parameters affect output.

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 function: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It includes a variety of trigger phrases and explicitly contrasts with sequential single-pack lookups, distinguishing it from sibling tools like entity_profile.

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 provides explicit usage guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and lists trigger phrases like 'X vs Y' and 'rank these companies.' However, it does not mention specific alternative tools by name or state when NOT to use this tool (e.g., for in-depth single-entity research), so it stops short of full when/when-not coverage.

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