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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, but the description adds substantial behavioral context: data sources (SEC EDGAR/XBRL, FAERS), fiscal-year handling (e.g., AAPL Sep, NVDA Jan), sorted results by primary metric, and return format (paired data + pipeworx:// citation URIs). This fully discloses what happens during execution without contradicting 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 but every sentence serves a purpose: trigger phrases, scope limits, data sources, sorting, return format, and efficiency gain. It is front-loaded with the most important usage cues and avoids redundancy. Slightly long, but for a tool that replaces many sequential lookups, this detail 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 correctly explains return values ('paired data + citation URIs') and behavioral guarantees (sorted results, fiscal-year correctness). It covers types, examples, size limits, and alternatives, making it complete for a complex comparison tool. No significant gaps remain.

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?

Although the input schema has 100% coverage with descriptions for both 'type' and 'values', the description enriches parameter meaning by specifying what each type returns: 'type="company" pulls LATEST 10-K revenue...' and 'type="drug" pulls FAERS adverse-event counts...' It also clarifies output sorting behavior, adding value beyond the schema's basic constraints.

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 the tool's purpose: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It enumerates trigger phrases like 'X vs Y' and 'rank these companies,' and distinguishes itself from single-entity lookups by noting it 'Replaces 8–15 sequential lookups.' This is a specific verb+resource with clear sibling differentiation.

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?

Usage guidance is explicit: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It defines when to use the tool (comparison, ranking, head-to-head queries) and provides conditional data specifics for company vs. drug types. This goes beyond implied usage and gives clear direction on prioritizing this tool.

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