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

Annotations already indicate the tool is read-only, open-world, and idempotent. The description adds significant behavioral details: it reveals data sources (SEC EDGAR/XBRL with off-calendar fiscal year handling, FAERS), mentions result sorting by primary metric, and specifies that it returns paired data with citation URIs. No contradictions with 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 well-structured, starting with common query examples, then stating the core function, followed by details on data sources and output. It is somewhat verbose but each sentence provides valuable information. Front-loading with examples aids quick understanding.

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?

Without an output schema, the description explains that it returns 'paired data + pipeworx:// citation URIs per entity,' which is sufficient for an agent to understand the return format. It also covers input constraints (2-5 entities) and data sources. Could mention if there are limits on the number of metrics returned, but overall complete for the tool's purpose.

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% with descriptions for both parameters. The description enriches these by explaining what data is pulled for each entity type (e.g., revenue, net income for companies; adverse-event counts for drugs) and provides examples of valid inputs (e.g., tickers, drug names). This adds meaningful context beyond the schema alone.

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 that the tool performs side-by-side comparisons of 2–5 companies or drugs, citing specific data sources (SEC EDGAR/XBRL for companies, FAERS for drugs) and examples of queries it handles (e.g., 'Compare X and Y', 'X vs Y'). It effectively distinguishes itself from sibling tools like 'entity_profile' by emphasizing its multi-entity parallel nature.

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 explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing a clear directive for when to use this tool versus making multiple calls to single-entity lookups. It also guides the user on the 'type' parameter for choosing between company and drug comparisons.

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