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

Annotations already declare readOnlyHint=true, but the description adds substantial behavioral detail: it pulls specific financial metrics (revenue, net income, cash, long-term debt) from SEC EDGAR/XBRL for companies, handles off-calendar fiscal years, and for drugs pulls FAERS counts, FDA approvals, and trial counts. It also discloses sorting behavior and the return of citation URIs, going far beyond the annotations. No contradictions found.

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 longer than average, but every sentence serves a purpose: trigger phrases, core function, preference rule, type-specific behavior, sorting, and return format. It is front-loaded with the most critical info (what it does and when to prefer it), and the density is justified by the tool's complexity. Slightly wordy but not wasteful.

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?

Given no output schema, the description compensates by explaining return values ('paired data + pipeworx:// citation URIs per entity') and sorting behavior. It also covers data sources and fiscal-year edge cases, making the tool's behavior clear for an agent. The 2–5 entity limit is already in the schema, so no gap there. The description fully covers the practical context needed to invoke the tool 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?

Schema coverage is 100% and already documents both parameters (type enum, values array with min/max). The description adds extra meaning by linking type='company' to specific financial data sources and type='drug' to adverse-event and approval counts, plus giving concrete value examples (AAPL, MSFT, ozempic). This enhances parameter understanding beyond the schema, though not maximally since the schema was already quite informative.

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 and resource: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It provides multiple trigger phrases ('compare X and Y', 'X vs Y', 'rank these companies') that make the tool's scope unmistakable. This distinguishes it from sibling tools like entity_profile, which handles single-entity 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?

The description explicitly instructs to 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' giving clear when-to-use guidance. It also explains the two modes (company vs drug) and indicates it replaces 8–15 sequential lookups, effectively stating when this tool is the superior choice.

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