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

With no annotations, the description carries full burden. It discloses that profiles are 'curated', scoped to what the fit engine knows, and lists the exact data dimensions returned, including usable memory after margins. For a zero-parameter list operation, this adequately sets expectations without hiding important behavior.

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 short sentences with no filler. It front-loads the resource and key fields, then adds the actionable follow-up instruction. Every clause contributes useful information.

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 simple zero-parameter list tool with no output schema, the description covers the scope (GPU/Mac), the returned fields, and the intended usage with find_fit. It omits formatting and pagination details, but those are not essential for this operation.

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 the schema is trivially complete and the description has nothing to add at the parameter level. The baseline of 4 for zero-parameter tools applies.

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 that the tool provides curated GPU and Mac profiles known to the fit engine, with fields like ids, memory, usable memory, and bandwidth. The verb is implied by the title and the resource is unambiguous, but it does not explicitly differentiate from sibling tools such as gpu_prices or trending_models.

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 gives a clear follow-up instruction: 'Use an id with find_fit', which tells the agent what to do after listing. However, it does not explicitly state when to prefer this tool over the alternatives or provide exclusions, leaving some usage context to inference.

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