validate_model

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

Annotations already declare readOnlyHint=true, and the description adds that it surfaces errors and warnings. However, it does not disclose any additional behavioral traits like whether it may be expensive or how it interacts with unsaved changes; the read-only nature is covered by the annotation.

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?

Single sentence, directly states the action and outcome. Fully front-loaded 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?

Tool has simple semantics, read-only annotation, and an output schema to document results. The description is sufficient but could benefit from a note on when validation is appropriate (e.g., before simulation).

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 covers 100% of parameter descriptions, including model_id defaulting to current model. The description's mention of 'current model' aligns with the schema but doesn't add new semantics beyond it.

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?

Description uses specific verb 'Validate' and resource 'model', clearly indicating it checks for errors and warnings. It is distinct from sibling tools like inspect_model or read_model, which focus on examination rather than validation.

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?

No guidance on when to use validate_model versus alternatives such as build_model or simulate. The description implies 'current model' context but does not state prerequisites or exclusions.

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