measure_uniformity

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

With no annotations, the description carries the full behavioral burden Committee. It goes beyond the schema by explaining the computation method ('4-corner median stddev') and the score direction ('Lower score means more uniform'). However, it does not disclose failure modes, return format details, or explicitly confirm read-only behavior.

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 short sentences with no filler. The core purpose and score interpretation are front-loaded, and every phrase adds value.

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?

This is a simple two-parameter measurement, and the description covers what is measured, the method, and how to interpret the result. The lack of an output schema is partly offset by the word 'score', though an explicit return-type note would make it fully 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?

Schema description coverage is 100%, so both view_id and sample_size are already documented. The description does not add meaning beyond the schema; it merely implies the corner-sampling context that the schema already states for sample_size.

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 ('Measure'), a clear resource ('background uniformity'), and a distinctive method ('4-corner median stddev'). This distinguishes it from sibling measurement tools such as measure_stars, measure_ringing, and measure_sharpness.

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?

There is no guidance on when to choose this tool over the many other measure_* siblings, nor any preconditions like needing an open view. The intended use is only implied by the tool name and the phrase 'background uniformity'.

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