Get All Makes

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

With no annotations, the description carries full responsibility for behavioral disclosure. It explains the curation filter, that all vehicle types are included, the exact return shape (list of dicts with id and name), and alphabetical sorting — substantial transparency for a listing endpoint.

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 compact and front-loaded with the core purpose, then scopes the data ('all vehicle types', 'curation filter'), and finishes with a precise return contract. The only minor overlap is between 'registered in vPIC' and 'curated database,' but both add distinct context.

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 list endpoint, the description is complete: it states what data is returned, the inclusion criteria, the output structure, and ordering. The presence of an output schema further covers return details, and no behavior needed to invoke the tool is missing.

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?

There are zero parameters and the schema already covers 100% of properties, so the baseline is 4. The description adds no parameter syntax, which is appropriate because no parameters exist.

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 opens with a specific verb and resource: 'List all vehicle makes registered in vPIC.' It reinforces the scope with 'every make currently in the curated database' and the return structure, making it easy to distinguish from siblings like get_models_for_make or decode_vin.

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 tool's zero-parameter, all-makes purpose makes its usage context clear, but the description does not explicitly name alternatives or give when-not-to-use guidance. Since siblings are all more specific (decoding VINs, model lookups), the intended use is still evident without exclusion statements.

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