nhtsa_makes

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

The annotations declare readOnlyHint=true, so the agent knows it's a safe read operation. The description adds the filtering behavior (makes with recalls/complaints) but does not disclose return format or edge cases. It does not contradict annotations.

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 short lines: purpose, parameter clarification, and an example. No wasted words, front-loaded with the core purpose.

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 simple two-parameter read-only tool with clear annotations, the description is adequate. It lacks explicit return structure, but the name and purpose imply a list of makes, which is sufficient for this simplicity.

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 description coverage is 100% – both parameters have descriptions. The description reiterates the issue_type values ('r' for recalls, 'c' for complaints) and provides an example, but this adds only marginal illustrative value beyond the schema.

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 'List vehicle makes for a model year that have recalls or complaints,' specifying the action (list) and resource (vehicle makes) with a precise scope (model year and issue type). This distinguishes it from siblings like nhtsa_models and nhtsa_model_years.

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 clear context on when to use the tool: when you need makes for a given model year filtered by recalls or complaints. The example illustrates usage, though it does not explicitly mention alternatives or exclusions. This is sufficient for a simple list tool.

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