palette_slop_implDetect AI-slop signatures like training-median hexes, purple gradients, flat lightness, and all-loud palettes to avoid generic AI-generated color schemes.
Detect AI-slop signatures: training-median hexes, purple gradients, flat lightness, all-loud palettes.
| Name | Required | Description | Default |
|---|---|---|---|
| palette | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It usefully enumerates the signatures it detects, implying a read-only analysis, but says nothing about the expected input format, whether it returns counts/flags/severity, or any constraints on the palette string.
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?
A single front-loaded sentence with no filler, packing four concrete detection criteria. Slightly dense jargon ('training-median hexes') but every clause earns its place.
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?
An output schema exists, so return values need no explanation. However, for a tool whose only input is an unformatted string, the description omits the input contract and offers no routing guidance among twelve sibling palette tools.
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 coverage is 0% and the single 'palette' parameter has no type detail beyond 'string'. The description never clarifies the accepted format (hex array, comma-delimited list, JSON), so an agent cannot know how to construct the value from either the schema or the description.
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?
Specific verb ('Detect') plus a named target ('AI-slop signatures') with concrete exemplars (training-median hexes, purple gradients, flat lightness, all-loud palettes). It is clearly distinct in kind from siblings like palette_score_impl or palette_match_impl, though it never explicitly names a sibling to differentiate itself.
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 when-to-use guidance, no condition that selects this over palette_score_impl or palette_semantic_impl, and no stated prerequisites. The intended usage (auditing a palette for AI-generated clichés) must be inferred from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.