list_hardware

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

With no annotations, the description carries the transparency burden. 'Summarize' signals a non-mutating operation, and the fallback ('CPU if none') plus output fields such as primaryBackend and vendor Docker images disclose key behavior. It does not discuss performance, privileges, or exact return structure, but those are less critical for a zero-parameter read-only listing tool.

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?

One sentence front-loads the action and scope, then packs the relevant output details without filler. No wasted clauses.

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 zero-parameter tool with no output schema, the description names the reported fields (VRAM, primaryBackend, Docker images) and the fallback case. It is complete enough for an agent to invoke and interpret the result, though it does not define primaryBackend or the exact response shape.

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 has no parameters, so there is no parameter semantics burden; the description adds the expected output context. Baseline of 4 is appropriate.

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?

States a specific verb ('summarize'), a defined resource ('discrete GPUs'), and a use context ('local model serving'); enumerates vendor scope and return fields, so it is clearly distinguishable from sibling tools, none of which list hardware.

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 phrase 'for local model serving' establishes when the tool is relevant, and the focus on GPUs plus CPU fallback makes its role clear among model-serving siblings. It does not explicitly rule out alternatives or name a sibling, so it stops short of a 5.

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