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

Beyond the readOnlyHint and idempotent annotations, the description reveals key behaviors: it pulls specific metrics (10-K revenue, net income, cash, long-term debt for companies; FAERS, FDA approval, trial counts for drugs), handles off-calendar fiscal years correctly, sorts results by primary metric, and returns paired data with citation URIs. This is rich, non-obvious behavioral detail.

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 longer than average, but every sentence adds operational value—trigger phrases, data-source details, fiscal-year handling, sorting, and output specifics. It is front-loaded with the most actionable guidance ('ALWAYS PREFER...'), though slightly dense; a bit of trimming could improve scannability without losing info.

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 there is no output schema, the description compensates by explaining what the response contains (paired data + citation URIs) and how results are ordered. It also covers edge cases (fiscal-year differences) and explicitly contrasts with the sequential-lookup alternative, making the tool fully self-contained for an agent to use 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?

Although the schema already documents both parameters with 100% coverage, the description adds substantial meaning: it maps 'type' values to distinct data sources and metrics, and elaborates on the 'values' format with examples (tickers vs drug names) and constraints (2–5 items). This goes well beyond the schema's basic 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 uses a specific verb ('compare') and names the exact resources ('2–5 companies or drugs') in a side-by-side parallel call. It clearly distinguishes itself from the sibling tool 'entity_profile' by emphasizing multi-entity comparison in a single call, 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?

It explicitly states when to use this tool: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also provides concrete trigger phrases ('X vs Y', 'rank these companies') and clarifies the distinction between company and drug types, giving clear context for selection versus alternatives.

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