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

Annotations already declare readOnlyHint and idempotentHint, so the description adds value by disclosing the sampling behavior and the condition under which complete spend is compared. This is beyond what annotations provide and is important for correct invocation.

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 extremely concise—two sentences with no wasted words. It front-loads the core purpose and adds a critical limitation, making it easy to parse.

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 the tool has an output schema and 5 parameters, the description covers the main behavior and a key limitation. However, it omits details about optional parameters like payment_type, which could be relevant for filtering. Still, it is fairly complete for a tool with rich structured metadata.

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?

With 0% schema description coverage, the description should compensate but only provides high-level context for companies and years. It does not explain parameters like payment_type or sample_per_year. Baseline 3 is appropriate given the partial coverage.

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 that the tool compares 2-5 company-name queries across CMS Open Payments program years using match counts and bounded samples. This distinguishes it from sibling tools that focus on single entities or other breakdowns.

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 provides a key caveat about sample size limiting completeness, but it does not explicitly guide when to use this tool versus alternatives like open_payments_company or open_payments_search. Usage context is implied but not fully explicit.

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