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

With no annotations, the description carries the full burden. It usefully discloses two key traits, that the call is free and unauthenticated, but says nothing about sample size, rate limits, or the shape/content of the returned rows, so the behavioral picture is only half drawn.

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?

Two short sentences with zero filler; the core trait (free, unauthenticated sample) is front-loaded and the rationale follows immediately.

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 zero-parameter tool with no output schema, the description covers cost and auth but leaves the returned content unspecified beyond 'carrier_profile rows', and does not resolve the overlap with get_identity_sample. Adequate, but missing context an agent would benefit from before calling.

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?

The tool takes zero parameters, so per the rubric the baseline is 4. There is nothing about parameters for the description to clarify or omit.

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 names a specific verb and resource ('sample of carrier_profile rows'), which is far more concrete than a tautology like 'gets a sample'. However, it does not distinguish itself from the near-twin sibling get_identity_sample, so an agent cannot tell from the description alone why both exist or how they differ.

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 clearly states the context of use: evaluate data quality before spending USDC on the paid tools. This effectively signals when to reach for this tool over the paid ones, though it never names a specific paid sibling or states a when-not condition.

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