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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description goes far beyond this by detailing exactly what data is pulled for each type (company: 10-K revenue/net income/cash/long-term debt from SEC EDGAR; drug: FAERS counts, FDA approvals, trials), how off-calendar fiscal years are handled, and that results are sorted by primary metric. It also discloses the output format (paired data + citation URIs). This is rich behavioral context with no contradictions.

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 compact yet information-dense. It front-loads the core purpose and trigger phrases, then systematically covers data sources, sorting, and output format. Every sentence earns its place; there is no filler or repetition of what the schema already provides. The length is appropriate for a tool with this complexity.

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 tool with only 2 parameters, no output schema, but non-trivial behavior, the description is remarkably complete. It covers supported entity types, data sources, fiscal-year handling, sorting, and output citation URIs. It gives enough context for an agent to correctly invoke the tool without needing external documentation, making it fully contextualized.

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%, so the baseline is 3. The description adds significant meaning beyond the schema: for 'type' it explains what each enum value actually retrieves, and for 'values' it gives concrete examples and clarifies output sorting behavior. It doesn't cover edge cases like invalid tickers, but the added context is clearly valuable.

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 trigger phrases and explicitly states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It clearly identifies the verb (compare), resource (companies/drugs), and scope, while distinguishing itself from sequential single-entity lookups. This fully achieves purpose clarity.

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 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and lists concrete trigger phrases like 'X vs Y' and 'rank these companies.' It also contrasts the tool against sequential lookups, making when-to-use and when-not-to-use unambiguous, even though a specific sibling name isn't given.

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