Catalog (freely callable)

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds non-redundant behavioral context: the result shape (slugs, names, year ranges), the ability to filter by year, and a suggested next step. No contradiction with 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?

Three short sentences front-load the core purpose, then add filtering and next-step guidance. Every sentence earns its place with no filler.

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 read-only catalog tool with a full input schema, output schema, and rich annotations, the description plus schema fully covers when and how to use it. The next-step hint completes the workflow context.

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 coverage is 100% and all parameters have descriptions, so the baseline is 3. The description adds a clarifying example for the year filter and emphasizes 'make' but does not materially enhance lang/region semantics.

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 action and object: 'List models for a given make.' It clarifies the output (model slugs, names, production year ranges) and positions the tool within the ws_list_* family, distinguishing it from makes/years/generations endpoints.

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 provides clear usage context with a concrete year-filter example and explicitly recommends downstream tools ('use ws_list_years or ws_list_generations next'). It stops short of describing exclusions or direct alternative list-model tools, but the workflow guidance is strong.

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