validate_all

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

No annotations are provided, so the description carries the full burden. It discloses a key behavior (validates all discovered models) and output format (per-model issues, summary counts), but lacks details on side effects, performance implications, or whether permissions are needed. This is adequate but not rich.

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 a single sentence that efficiently conveys the tool's purpose and return value. Every word is necessary, and there is no redundancy or 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 tool with no parameters and no output schema, the description covers the core functionality and return format. It could be slightly more explicit about the structure of per-model issues, but overall it provides sufficient context for an agent to understand when and how to use it.

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 input schema has zero parameters with full coverage (trivially 100%), so the description does not need to add parameter details. Per guidelines, a baseline of 4 applies for tools with no parameters, and the description provides no conflicting or additional semantic information.

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 'runs validate_model across every discovered model', specifying the action (validate) on the resource (all models). It returns per-model issues and a summary count, making it specific and distinguishable from sibling 'validate_model', which likely operates on a single model.

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 implies the tool is for bulk validation, but it does not explicitly state when to use it versus alternatives like 'validate_model'. There is no guidance on when not to use it or any prerequisites, leaving the agent to infer the context.

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