system_resources

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

The description implies a read-only, safe operation, but with no annotations, it lacks details on execution speed, side effects, or permission requirements. It does not contradict anything as there are no annotations.

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 sentences: one lists what is reported, the second provides the use case. No fluff; each sentence serves a purpose.

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 parameterless tool without output schema, the description fully covers the tool's purpose and the data it returns. It is complete enough for an agent to decide when to call it and understand the response.

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?

With zero parameters and 100% schema coverage, the description adds value by explaining the output content (platform, RAM, CPU, GPUs). The baseline for no params is 4, and the description meets that.

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 reports local hardware resources, listing specific items (platform, RAM, CPU, GPUs). It distinguishes this tool from siblings like health_check or benchmark by focusing on static hardware info.

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 'Used to reason about which models can run locally' provides clear context for when to use this tool. However, it does not explicitly discuss when not to use it or compare to sibling tools like fit_check.

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