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

Goes far beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) by disclosing data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years (AAPL Sep, NVDA Jan), sorting behavior, and return format (paired data + pipeworx:// citation URIs). 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?

The description is dense yet well-structured, front-loaded with user-intent examples for instant recognition. Every sentence serves a purpose: what, when, data specifics, sorting, return format, and efficiency gain. No filler or repetition.

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?

For a 2-parameter tool with no output schema, the description covers all essential context: input patterns, parameter behavior, data sources, edge cases (fiscal year alignment), output ordering, and paired result structure. It leaves no ambiguity about what the tool does or returns.

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 schema covers both parameters (type, values) at 100%, the description enriches semantics by explaining what data each type pulls (10-K metrics vs FAERS counts), clarifying the expected value formats (tickers/CIKs vs drug names), and giving concrete examples. This adds meaning beyond the schema's bare enumeration.

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 concrete query patterns ('Compare X and Y', 'X vs Y', 'which is bigger') and states the core function: side-by-side comparison of 2-5 companies or drugs in one parallel call. It clearly distinguishes itself from sequential single-pack lookups and explicitly mentions replacing 8-15 such lookups, differentiating it from sibling tools like entity_profile.

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 defines what each type pulls (company vs drug) and specifies the primary-metric sorting, so an agent knows exactly when to invoke this tool over alternatives.

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