sensor_report

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

Without annotations, the description discloses key behavioral traits: raw input is smoothed via time-aware exponential smoothing, the stored state updates, and the smoothed state is returned. It does not cover permissions or errors, but the scope is adequate for a straightforward sensor reporting 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?

The description is concise and well-structured: it starts with the action, explains the smoothing behavior, and ends with usage examples. Every sentence adds value without redundancy.

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 and lack of output schema, the description covers the purpose, parameter constraints, behavioral details, and return value. It does not mention error conditions or prerequisites, but it is sufficiently complete for an AI agent to use correctly.

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 0% schema description coverage, the description fully compensates by explaining that 'name' must be 'stress' or 'room_intensity' and 'value' is a raw observation between 0-100. This adds crucial semantics beyond the bare schema.

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's purpose: reporting a raw observation for a virtual sensor. It specifies the value range (0-100), mentions exponential smoothing, and gives concrete name examples ('stress', 'room_intensity'), distinguishing it from sibling tools like sensor_read.

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 provides clear guidance on when to use the tool (to report observations) and which names to use. It does not explicitly state when not to use it or how it differs from siblings, but the context is strong enough for an AI agent to decide appropriately.

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