read_humidity

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It communicates that the operation is a read (implying no side effects) and specifies the return unit (percentage). Minor gaps remain, such as not mentioning whether a connection is required, but for a simple read operation 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 two short sentences with no filler. The core action is front-loaded, and the return value is stated clearly. Every sentence earns its place.

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, no-output-schema tool, the description is complete: it names the source device, the operation, and the return format. An agent has enough information to invoke this tool correctly without additional context.

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 takes zero parameters, so the input schema is empty and there are no parameter semantics to explain. The description adds useful context by clarifying the return value's unit, which is more than required for a parameterless tool. Baseline 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?

The description uses a specific verb ('Read') and resource ('humidity from the Thingy device'), clearly distinguishing it from sibling sensor tools like read_temperature and read_pressure. It also specifies the return format ('Relative humidity in percentage'), making the tool's purpose unambiguous.

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 clearly implies usage when humidity is needed, but it does not explicitly state when to prefer this tool over alternatives or mention any exclusions/preconditions. Sibling tools like read_all_sensors could also provide humidity, so some guidance on when to use this specific tool would improve the score.

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