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

The readOnlyHint annotation already establishes this is a safe read. The description adds that the content is a static reference read once before writing UI, implying idempotent, cacheable content, which is a small but real behavioral signal. It says nothing about format or length of the returned rules, so value beyond annotations is modest.

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 compact sentences, front-loaded with what the tool is, then usage timing. The category list is terse to the point that phrases like 'shared state words' and 'touch and focus' are slightly cryptic, keeping it short of a 5.

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 reference tool, the description is sufficient to decide when to call it and what kind of content to expect. Minor gaps remain about the volume/applicability of the rules, but nothing essential is missing.

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 schema has nothing to document and the description bears no parameter burden. Baseline 4 applies.

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?

States a clear verb+resource ('the rules for building UI with Mlola') and enumerates the rule categories (compose, color, scales, state words, touch/focus), so an agent knows it returns design guidance rather than concrete artifacts. It is implicitly distinct from siblings like get_tokens and get_component, but it never explicitly contrasts with them.

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?

'Read once before writing UI' gives clear when-to-use timing. There is no negative guidance (e.g. 'not needed for one-off tweaks') and no named alternatives, but the context for invoking it is explicit.

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