List decision models

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

With no annotations, the description carries the burden and does disclose the read-only nature implicitly plus concrete return content and the default-model flag. It omits auth/rate-limit or freshness details, but for a zero-parameter listing tool the behavioral gap is minor.

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, output contents front-loaded, then the downstream usage note, then a sizing hint. Nothing repeats the name or title and no sentence is padding.

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?

No output schema exists, so the description usefully enumerates the return fields and the default indicator. Combined with the cross-tool `model` guidance, an agent has enough to call it and use the result correctly; only pagination/scale behavior is unstated.

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?

Zero parameters gives a baseline of 4. The description goes slightly further by explaining the cross-tool convention for passing a returned id as `model`, which is useful semantics the empty schema cannot express.

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?

States a specific verb+resource ('List the decision models') and enumerates the returned fields (id, context window, input price) plus the default marker, so an agent knows exactly what it gets. It does not name the sibling tools, but their names (classify/locate/decide) make the distinction obvious without it.

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?

'Pass an id as `model` to any other tool' clearly frames this as the discovery step that precedes every other call. It gives a positive usage context but no explicit when-not or prerequisites (e.g. caching, freshness).

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