list_palettesBuilt-in curated colour palettes with their hex colours and derived mood tags (dark, pastel, vivid, warm, cool…). Pass the id as palette.palette to generate_background.
| Name | Required | Description | Default |
|---|---|---|---|
| mood | No | Filter by a mood tag, e.g. "dark" or "pastel". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-open-world behavior, so the bar is lower. The description adds useful context: palettes are built-in and curated, the response contains hex colours and derived mood tags, and the output id feeds generate_background. No contradiction with annotations.
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, each earning its place: the first defines the resource and its contents, the second explains how to use the result. No redundant wording or filler.
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 simple optional-filter list tool with read-only/idempotent annotations and no output schema, the description tells the agent what to expect (palettes with hex, mood tags) and how to consume the result (id mapping). This is sufficient for correct invocation.
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 schema already documents the optional mood parameter with examples, giving 100% coverage, so the baseline is 3. The description adds a few more mood tag examples and notes they are derived, but this is marginal enrichment rather than essential meaning.
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 clearly states the tool lists 'built-in curated colour palettes' with hex colours and derived mood tags, using a specific verb and resource. It also distinguishes the scope from background-oriented siblings by focusing on palettes and explicitly pointing to generate_background.
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?
The phrase 'Pass the id as palette.palette to generate_background' gives a clear downstream workflow, showing when this tool is useful. It does not name sibling alternatives or state when not to use it, but the intended context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct task: browsing catalog, inspecting a structure, generating output, listing palettes, listing brand kits, and suggesting options. list_brand_kits and list_palettes are the only superficially similar pair, but their descriptions clearly separate saved account kits from built-in palettes.
All tools use the same verb_noun snake_case convention: describe_, generate_, list_, and suggest_. Pluralization is consistent within each verb's usage, making the naming predictable and easy to infer.
Six tools is well-scoped for a background generation service. Discovery, description, generation, suggestion, and color inputs are each represented without redundancy or excessive surface area.
The tool set covers the full consumer workflow: browse backgrounds, understand a structure, generate an image, and supply palettes or brand kits. The only potential gap, managing brand kits, is outside the server's stated purpose.