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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description goes well beyond this by revealing specific data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, sorting by primary metric, and the return format (paired data + citation URIs). This rich behavioral context helps the agent anticipate outputs and limitations.

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 examples, usage guidance, data source specifics, and output behavior. It is appropriately front-loaded with the most critical information. While slightly long, the complexity of the tool justifies the length, and nothing feels like 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 the tool's moderate complexity (2 params, no output schema), the description covers all essential aspects: what it does, when to use it, what data it accesses, how results are sorted, and what output format to expect (paired data + citation URIs). It also quantifies the efficiency gain ('Replaces 8–15 sequential lookups'), making the tool's value proposition clear. No critical 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?

Schema coverage is 100% with both parameters described, providing a baseline of 3. The description adds substantial value by explaining that type='company' pulls specific financial metrics (revenue, net income, cash, long-term debt) and type='drug' pulls FAERS counts, along with concrete examples for the values parameter. This goes beyond the schema's generic descriptions.

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 explicit trigger phrases ('Compare X and Y', 'X vs Y', 'which is bigger') and clearly defines the tool's core function: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It distinguishes itself from sequential single-pack lookups, making the purpose unmistakable.

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 when-to-use guidance through example queries and states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' naming an alternative approach. However, it doesn't explicitly state when NOT to use this tool (e.g., for a single entity), though that is implied by its naming of the alternative for multi-entity comparisons.

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