measure_bright_chroma

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

With no annotations, the description carries the full disclosure burden, and it succeeds: it reveals the sampling scheme (every 8th pixel), the selection condition, the exact return JSON keys, and the mono-view error condition. This gives an agent a clear model of what will happen when the tool is invoked.

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 deliver the essential information with no filler: formula, pixel filtering, output shape, and error condition. The metric definition is front-loaded and the return format follows logically.

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?

Despite having no output schema, the description lists the exact JSON fields returned. Both required parameters are documented, the sampling behavior is specified, and the only obvious error case (mono view) is stated. For a simple read-only measurement tool with two parameters, this is complete.

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 already covers both parameters with 100% description coverage, so the baseline is 3. The description largely restates the schema's meaning for brightness_threshold and view_id, adding only the sampling behavior rather than new per-parameter semantics.

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 names a specific operation (measure chroma), a precise metric formula ((max - min) / max), a target population (bright pixels in a colour view), and an explicit sampling rule (every 8th pixel). This clearly differentiates it from sibling measurement tools and leaves no ambiguity about what the tool does.

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?

Usage is reasonably implied: call this when you need chroma statistics from bright pixels of a colour view, and the description explicitly warns that a mono view is an error. However, it does not compare against similar siblings such as measure_saturation or measure_uniformity, nor does it state when one should be chosen over another.

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