sensors_readout

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

No annotations are provided, so the description carries the full disclosure burden. It is transparent about the underlying command (`sensors -j`) and the output format (structured JSON), which sufficiently communicates the read-only, non-destructive nature of the tool. It does not discuss permissions or edge cases, but for a simple sensor readout this is adequate.

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 a single, well-structured sentence with no waste. It conveys the tool's purpose, output format, and underlying mechanism efficiently.

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 the tool's simplicity (no parameters, no output schema), the description is largely complete. It explains what is returned (all lm-sensors readings) and in what format (JSON). It could elaborate on the JSON structure (e.g., keyed by chip/adapter), but this is not critical for basic usage.

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, which meets the baseline of 4. The description adds context by specifying the exact command used, ensuring there is no ambiguity about how the readout is produced.

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 returns all lm-sensors readings as structured JSON, referencing the underlying `sensors -j` command. This specific verb+resource phrasing distinguishes it from siblings like cpu_temp and disk_temperatures, which target individual sensor categories.

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 'All lm-sensors readings' signals that this is the comprehensive sensor-data tool, implicitly contrasting with narrower siblings. However, it does not explicitly name alternatives or state when not to use it, 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.