load_checkpoint

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

With no annotations provided, the description must disclose behavioral traits itself. It mentions the side effect of loading into GPU memory and the benefit of speed, but doesn't cover failure modes, memory implications, or whether it replaces an existing checkpoint. This is acceptable but not comprehensive.

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?

Two short sentences with the action stated up front. No filler or redundant information. Every sentence contributes value: the first states the purpose, the second explains the benefit and gives a practical hint.

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 a single optional parameter and an output schema present, the description is nearly complete. It covers what the tool does, why to use it, and how to find valid inputs. It doesn't mention that pre-loading might be optional or that generation may auto-load, but this is a minor gap given the tool's simplicity.

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 provides 100% coverage of the parameter, including default value and examples. The description adds no additional parameter semantics beyond what the schema already documents. The pointer to list_models() is helpful but not required.

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 action: 'Pre-load a checkpoint into GPU memory.' This is a specific verb+resource combination that distinguishes it from siblings like list_models or generate_image. No other sibling tool performs checkpoint loading, so there is no ambiguity about its purpose.

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?

It explicitly says loading in advance speeds up generation calls, which implies it should be used before generate_image. It also directs users to list_models() to discover valid checkpoints, providing practical usage guidance. It doesn't mention when not to use it, but for a simple pre-loading tool, this context is sufficient.

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