Turn vague design requests like "make it pop" into precise design terms and a brief.
Scored across 1 tool
With only one tool, there is no possibility of confusing it with another operation. The tool has a narrow, clearly stated purpose: translating vague design feedback into actionable design direction.
The single tool name is descriptive, uses clear snake_case, and includes the server prefix. There is no competing naming convention to create inconsistency.
The server is intentionally narrow, so one tool is defensible, but the surface feels thin for a design-request translator. Companion capabilities such as listing supported phrases or batching multiple requests would make the toolset feel more complete.
The core workflow is covered: vague phrase in, concrete design moves and a ready-to-use brief out. Minor gaps remain around discovering supported phrases, handling multiple requests, or refining an existing brief, but agents can work around those limitations.
Use when a user (often not a designer) gives vague design feedback or a request such as "make it pop", "more premium", "cleaner", "less boring", "feels off" and you need specific, actionable design changes rather than guesses. Matches the phrase to design terms and returns what it usually means, concrete moves (spacing, contrast, type, color, hierarchy) and a ready-to-use design brief, with an option to avoid generic AI-looking choices. Example: {"phrase": "make it pop"}.
| Name | Required | Description | Default |
|---|---|---|---|
| phrase | Yes | The vague request in plain words, e.g. "it feels cheap and too busy". | |
| avoid_ai_look | No | Add rules that steer away from the generic AI-made look. Default true. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this is a read-only, idempotent, non-destructive, closed-world operation, so the safety profile is covered. The description adds genuinely useful behavioral detail the annotations cannot: the return payload (phrase meaning, concrete moves across spacing/contrast/type/color/hierarchy, and a ready-to-use brief) and the existence of a steer-away-from-AI-look mode. It stops short of describing the exact output shape, which is a minor gap given no output schema exists.
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?
Front-loaded with the triggering condition, then the mechanism, then the payoff, then a compact example. Despite being a single dense paragraph, every clause adds information and nothing is redundant padding.
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 two-parameter, read-only tool with no siblings and no output schema, the description covers the trigger, the transformation, and the general shape of the return value, which is nearly everything an agent needs. The only remaining gap is the exact structure of the returned brief, which matters slightly more here because no output schema exists to document it.
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 the schema already documents both `phrase` and `avoid_ai_look` (including its default of true). The description's phrase examples and inline example call `{"phrase": "make it pop"}` illustrate usage but largely repeat what the schema already provides, so the baseline of 3 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?
The description states a specific transformation: a vague design phrase in, a concrete design brief out, naming exactly what the tool resolves ("matches the phrase to design terms"). It is unambiguous what the tool does and what an agent gets back, and the title reinforces rather than merely restates it.
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?
"Use when a user (often not a designer) gives vague design feedback..." gives an explicit trigger condition, backed by five concrete example phrases ("make it pop", "more premium", "feels off"). It also names the alternative behavior it replaces: producing "specific, actionable design changes rather than guesses." There are no sibling tools, so no further routing is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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