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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds valuable behavioral context: it executes in one parallel call, sorts results by the primary metric, returns paired data with citation URIs, and correctly handles off-calendar fiscal years (e.g., AAPL, NVDA). This goes well beyond the annotations without contradicting them.

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 appropriately sized for the tool's complexity and is well-structured: it starts with usage examples, then states the core capability, then per-type details, then output and efficiency benefits. Every sentence contributes useful information with no redundancy or filler.

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 only 2 parameters and no output schema, the description covers input semantics, output format (paired data + citation URIs), sorting behavior, and special-case handling (off-calendar fiscal years). It also conveys the tool's efficiency advantage, 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?

Schema description coverage is 100%, so the baseline is 3. The description goes further by explaining the meaning of each type value ('company' pulls 10-K revenue/net income/cash/debt; 'drug' pulls FAERS/FDA/trial counts) and provides concrete example arrays. This adds semantic depth that the schema alone does not convey.

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 defines the tool as a side-by-side comparison of 2–5 companies or drugs in a single parallel call. It lists multiple natural-language triggers ("X vs Y", "which is bigger", "rank these companies") and explicitly differentiates it from sequential single-pack lookups, leaving no ambiguity about its purpose.

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 provides explicit usage guidance: "ALWAYS PREFER over sequential single-pack lookups when comparing entities." It also clarifies when each type (company vs drug) is appropriate and what metrics are pulled. However, it does not explicitly mention when to choose an alternative like entity_profile or deep_research, so it falls slightly short of fully covering when-not-to-use scenarios.

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