gpu_memory_map

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

No annotations are provided, so the description carries the full burden. It discloses that the tool is a read operation ('See exactly...') and mentions a dependency. However, it does not disclose authentication needs, rate limits, or any potential side effects. The requirement of GPU optimizer is helpful context.

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 consists of two short, directly relevant sentences. The first sentence states the purpose, the second adds a requirement. No extraneous information, well front-loaded.

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 no parameters, no output schema, and no annotations, the description is sufficient for basic understanding but lacks details on output format, error scenarios, or performance implications. It is adequate but leaves room for more completeness.

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, so schema coverage is 100% by default. The description adds meaning by stating what the tool returns: models, sizes, and devices. This baseline of 4 is appropriate as the description compensates for the lack of parameter details.

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 clearly states the tool shows which models are loaded on GPU, their sizes and devices. It uses a specific verb 'See' and identifies the resource. This distinguishes it from siblings like gpu_status which likely show overall memory usage rather than per-model mapping.

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 mentions a prerequisite ('Requires the GPU optimizer inside ComfyUI') but does not provide guidance on when to use this tool versus alternatives such as gpu_status, gpu_processes, or other GPU tools. There is no explicit when-not or alternative naming.

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