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

Beyond the annotations (readOnly, idempotent, etc.), the description discloses key behavioral traits: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), handling of off-calendar fiscal years, sorting by primary metric so 'largest' reads off the top, and return format with pipeworx:// citation URIs. This is rich, non-obvious context that annotations do not provide.

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 somewhat long but front-loaded with example queries and a clear purpose statement. Every sentence adds unique value (data sources, sorting, citations, efficiency gains). It could be trimmed slightly, but it remains efficient and well-structured for a tool with two distinct modes.

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 covers return values ('paired data + pipeworx:// citation URIs'), data sources for both types, sorting behavior, and fiscal year handling. It also explains the tool's advantage over alternatives, making it fully self-contained for an agent to decide when and how 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?

Though schema coverage is 100%, the description adds significant meaning: the 'type' parameter is tied to specific data fields (revenue, net income, cash, long-term debt for company; adverse-event counts, approval counts, trial counts for drug). It also clarifies that values are tickers/CIKs vs drug names, and gives concrete examples. This elevates understanding well beyond the raw schema.

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 a specific verb+resource: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It opens with example queries ('Compare X and Y' / 'X vs Y') that immediately establish intent, and distinguishes itself from sibling tools by explicitly saying 'ALWAYS PREFER over 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?

Provides explicit when-to-use guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also names the alternative approach (sequential lookups) and explains why this tool is better ('Replaces 8–15 sequential lookups'), making the usage context unmistakable.

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