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

The description goes far beyond the annotations by detailing exact data sources (SEC EDGAR/XBRL, FAERS), specific metrics (revenue, net income, cash, long-term debt, adverse-event counts, FDA approval counts, trial counts), handling of off-calendar fiscal years, sorting behavior by primary metric, and output format with citation URIs. This is rich behavioral context.

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 carries useful information. It opens with trigger phrases, then states the preemptive usage preference, followed by data source specifics, sorting behavior, and output details. No filler or redundancy.

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 two parameters and no output schema, the description covers all essential aspects: input constraints (2–5 entities), entity types, data sources, sort order, output format (paired data + citation URIs), and performance benefit. It is fully self-contained 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% but the description adds meaning beyond the schema by explaining what each 'type' value retrieves (company financials vs drug trial/adverse-event data) and gives concrete examples like 'AAPL', 'MSFT', 'ozempic', 'mounjaro'. It also clarifies result sorting, which helps agents understand parameter implications.

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 comparison of 2–5 companies or drugs in one parallel call, with explicit trigger phrases like 'Compare X and Y' and 'X vs Y'. It also distinguishes itself from siblings by contrasting with 'sequential single-pack lookups' and stating it 'Replaces 8–15 sequential lookups'.

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 says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing a strong usage directive. It also lists specific query types that should invoke this tool and implies a limit of 2–5 entities, giving clear context for when to use it.

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