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

Annotations declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds valuable behavioral context: it discloses data sources, explains how fiscal year variations are handled, notes results are sorted by primary metric, and mentions 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 a single paragraph that efficiently packs multiple pieces of information: triggers, use preference, entity types, data details, and output format. It is slightly dense but front-loaded with the most critical usage triggers.

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?

Despite having no output schema, the description fully covers the tool's functionality: input constraints (2-5 entities), data content per type, sorting behavior, and return format (paired data + URIs). It also explains edge cases like off-calendar fiscal years, making it complete for the tool's complexity.

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?

Input schema coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining what data is retrieved for 'company' vs 'drug' types and how results are sorted. This provides richer context for parameter selection.

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 states the tool performs side-by-side comparisons of 2-5 companies or drugs, specifying the data sources for each type (SEC EDGAR/XBRL for companies, FAERS for drugs). It also lists natural language triggers like 'compare X and Y' and 'rank these companies,' 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 explicitly advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear guidance on when to use this tool over alternatives like entity_profile. It does not list explicit when-not-to-use scenarios, but the strong preference is well communicated.

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