Get validation

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

With no annotations, the description carries full responsibility for behavioral disclosure. It states the scope ('whole selected model') but does not mention whether the operation is read-only, what the findings contain, how they are formatted, or what happens if no model is selected. This is insufficient for a tool with no other safety indicators.

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 concise sentence with no wasted words. It is front-loaded with the key concept ('Validation findings') and directly communicates the tool's purpose.

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?

The description is complete enough for a simple 0-parameter retrieval tool, covering the core purpose. However, without an output schema or annotations, it leaves gaps about the content and format of validation findings, and how they relate to the selected model. This is a minimum viable description but lacks context to fully guide the agent.

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 tool has 0 parameters, and the input schema is empty. According to the guidelines, the baseline for 0 params is 4, and there is no parameter-level detail missing. The description adds no parameter semantics because there are none to document.

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 'Validation findings for the whole selected model' clearly identifies the resource (validation findings) and scope (whole selected model), distinguishing it from sibling tools like get_model or get_element. The verb is omitted but implied by the tool name 'get_validation', so the purpose is unambiguous.

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 usage via 'Validation findings' and 'selected model', indicating the tool is for retrieving validation results of the current model. However, it provides no explicit instructions on when to use this tool versus alternatives, nor any exclusions or prerequisites.

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