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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds value beyond annotations by listing exactly what information is returned (formats, counts, step range, base model, workflow, pricing). It does not contradict annotations and provides useful behavioral context about the tool's purpose, though it omits any mention of response structure or potential errors, which is acceptable given the read-only nature.

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 two sentences with zero waste. It front-loads the usage context ('Use this when...') and then lists the concrete outputs. Every clause earns its place, making it easy for an agent to quickly grasp the tool's purpose and when to invoke it.

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?

Given the tool has no parameters and an output schema exists (though not shown), the description covers all essential information an agent needs: the specific use case (image LoRA training without local GPU) and the full list of return values. It is complete for a read-only information retrieval tool. The output schema presumably defines the structure, so the description need not repeat that.

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 zero parameters, and the schema is empty. Per the rubric, a baseline of 4 applies. The description does not add parameter-specific semantics because there are none; instead, it clarifies the tool's output, which is the relevant semantic content. Since the schema offers no constraints or enums, the description compensates by explaining the tool's function and return values.

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's function: it returns training requirements for image LoRA models. It specifies the resource ('image LoRA training requirements') and the concrete outputs (formats, counts, step range, base model, workflow, pricing). This distinguishes it from generic training requirement tools like get_training_requirements and get_conversion_requirements by specifying 'image LoRA'.

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 explicitly states when to use it: 'when a user wants to train an image LoRA ... from their own images without a local GPU.' This is clear and actionable. However, it does not explicitly name alternatives or state when NOT to use it (e.g., for voice conversion or generic training). With 16 sibling tools, adding explicit exclusion would improve guidance, but the current guidance is sufficient for most cases.

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