get_sample

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

With no annotations provided, the description carries the full burden. It discloses the fixed sample vehicle, the live computation, and the free/no-auth behavior. It also elaborates on what each kind includes, which is helpful for setting expectations. It doesn't mention rate limits or error behavior, but for a sample endpoint that's acceptable.

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 three sentences, front-loaded with the core value proposition. The parameter details are compact but informative, and there is no 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?

The tool is simple with one parameter and no output schema. The description covers purpose, usage, and parameter specifics, and even hints at response shape composition. For a sample-preview tool, this is sufficiently complete.

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 schema already documents the 'kind' parameter with an enum, so baseline is 3. The description adds semantic context for each enum value, such as 'decode (incl. factory-warranty block)' and 'purchase-costs (returns both CA + US),' which goes beyond the schema. This enrichment justifies a higher score.

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's function: fetching a free sample response for paid endpoint shapes, using a fixed sample vehicle. It distinguishes itself from siblings like decode_vin or get_recalls by explicitly noting it's a sample before paying. The verb+resource+scope is specific.

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 says 'Use this to see the exact response shape before paying,' giving clear when-to-use context. It also states it's free and requires no wallet or API key, which implies it's for evaluation rather than production. However, it doesn't explicitly name the alternative tools for actual data, leaving some ambiguity.

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