check_model_integrity

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

With no annotations, the description carries the full burden and does reveal the tool runs the same checks as check_silent_failures and returns a compact text report. However, it does not explicitly state that the operation is read-only/non-destructive, nor describe error behavior or edge cases, leaving some behavioral ambiguity.

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 concise, front-loaded with the core purpose, and each sentence contributes: what it checks, how it relates to the sibling, when to use it, args, and return. No filler or redundancy.

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 tool with two optional parameters, the description provides enough context to call it: usage timing, scope, and return format. The only notable omission is timeout parameter semantics, which prevents a perfect score.

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 0%, so the description must document both parameters. It documents model_name, but says nothing about timeout, its behavior, or acceptable values. Timeout is only partially self-explanatory, so this is a clear gap.

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?

Clearly states it performs a model integrity check on mesh, constraints, contacts, and volumes, and explicitly differentiates itself from check_silent_failures by being a fast subset. The scope is specific and an agent can immediately tell what it does.

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?

Explicitly recommends using it after building a model and before submitting a job, giving concrete workflow context. It also names the sibling check_silent_failures and positions itself as a faster, compact alternative, though it does not explicitly state when to choose the full version.

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