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

Annotations already declare read-only and idempotent behavior, but the description goes far beyond: it discloses data sources (SEC EDGAR/XBRL, FAERS), fiscal-year handling, sorting by primary metric, and the inclusion of citation URIs. This is rich behavioral context that significantly helps an agent predict what will happen, and it contains no contradiction with the 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 highly efficient: trigger phrases come first, then usage guidance, then behavioral specifics. Every sentence and clause earns its place, and despite being long, the structure is front-loaded with the most important information for tool selection.

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?

This is a complex tool with two entity types, multiple data sources, sorting behavior, citation returns, and no output schema. The description covers all these aspects, including a helpful note that it replaces 8–15 sequential lookups, making it nearly complete for an agent to invoke correctly.

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 meaning beyond the schema by explaining exactly what each type value ('company' vs 'drug') retrieves and how the results are sorted, which enriches the semantic understanding of the parameters. The values parameter is already well covered by schema examples, so the additional value is modest but real.

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 identifies the tool as a side-by-side comparison engine for 2–5 companies or drugs, with explicit trigger phrases and a strong distinction from sequential single-pack lookups. It names the verb ('compare'), the resource ('companies or drugs'), and the scope in a way that leaves no ambiguity.

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?

It provides explicit when-to-use guidance through natural-language examples and an outright directive to prefer this tool over sequential lookups for comparison tasks. However, it does not mention edge cases such as comparing more than 5 entities or non-company/drug types, so it falls just short of a perfect usage-guideline score.

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