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

The description discloses rich behavioral traits beyond annotations: it pulls data from SEC EDGAR/XBRL for companies and FAERS for drugs, handles off-calendar fiscal years correctly (AAPL Sep, NVDA Jan), sorts results by primary metric, and returns paired data with citation URIs. These details add substantial context on top of the readOnly/idempotent 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 and well-organized, front-loading trigger phrases and core purpose. There is minor redundancy between 'ALWAYS PREFER over sequential single-pack lookups' and 'Replaces 8–15 sequential lookups,' but each sentence adds relevant value, so the length is justified.

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 still covers the return format (paired data + pipeworx:// citation URIs), sorting behavior, and type-specific data sources. For a tool with only two parameters and no nested objects, this is complete enough for an agent to select and invoke correctly.

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?

Even though schema coverage is 100%, the description enriches parameter meaning by explaining what each type='company' or type='drug' pulls, and provides concrete examples for 'values' (tickers vs drug names). This goes well beyond the schema's terse field descriptions.

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 a side-by-side comparison of 2–5 companies or drugs in one parallel call, with concrete examples of user queries that trigger it. It distinguishes itself from siblings by naming 'sequential single-pack lookups' and preferring this tool for comparisons. The type-specific data sources (SEC EDGAR/XBRL for companies, FAERS for drugs) clarify what the tool does.

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 says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing a direct directive for when to use this tool versus alternatives. It also lists typical user phrasings like 'X vs Y' and 'which is bigger,' making the intended trigger conditions clear. This is strong guidance.

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