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

Beyond the readOnlyHint=true annotation, the description discloses specific data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handles edge cases (off-calendar fiscal years), and explains output traits like sorting by primary metric and returning paired data with citation URIs. This adds substantial behavioral context.

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 and front-loaded with trigger phrases, then provides concrete details about data sources, sorting, and output. It is longer than minimal but each sentence carries useful information; the structure is logical and scannable, though slightly verbose.

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 compensates by explaining the return format ('paired data + pipeworx:// citation URIs'), the metrics for each entity type, sorting behavior, and that it replaces 8–15 sequential lookups. This covers the necessary context for invocation and expected results.

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 clear descriptions for both 'type' and 'values'. The description adds example values and elaborates on what metrics are pulled, but this is more behavioral than parameter-level semantics. It does not significantly enhance meaning beyond the schema, so baseline 3 applies.

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 explicitly states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call' with a clear verb and resource. It also lists trigger phrases like 'X vs Y' and 'rank these companies' and distinguishes itself from sequential single-pack lookups.

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 guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' and clarifies when to use type='company' vs type='drug' with data source details. This directly addresses when to use the tool versus alternatives.

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