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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description goes beyond these by disclosing data sources (SEC EDGAR/XBRL, FAERS, FDA), off-calendar fiscal year handling, automatic sorting, and output of paired data with citation URIs per entity. 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?

At approximately 120 words, the description is reasonably compact and front-loaded with the most critical information. Each sentence adds distinct value: intent triggers, preference rule, type-specific behavior, sorting, and output format. It is slightly dense but well-structured without redundancy.

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?

Despite lacking an output schema, the description conveys the return shape ('paired data + pipeworx:// citation URIs per entity') and sorted results. It also covers fiscal-year handling for companies, which is a subtle but important detail. Minor gaps like error handling or unit formatting are acceptable given annotations and the simple parameter set.

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 already covers both parameters with descriptions and examples, so coverage is 100%. The description adds meaningful detail by explaining what each type retrieves: company pulls 10-K financial metrics, drug pulls FAERS counts and FDA/trial data. It also reinforces that 'values' expects tickers/CIKs vs drug names.

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 opens with user-intent phrase patterns ('Compare X and Y', 'X vs Y', etc.) and states exactly what the tool does: side-by-side comparison of 2–5 companies or drugs in one parallel call. It clearly distinguishes itself from sequential single-pack lookups and uses specific verbs like 'pulls', 'returns', and 'sorted' to define its scope.

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?

Explicitly instructs 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and states it 'Replaces 8–15 sequential lookups'. It provides actionable context for when to use company vs drug types and what to expect from each, making the selection criteria unambiguous.

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