List brand kits
list_brand_kitsSaved brand palettes on this account. Pass a kit's slug as brand to generate_background to render on-brand without repeating hex colours.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
list_brand_kitsSaved brand palettes on this account. Pass a kit's slug as brand to generate_background to render on-brand without repeating hex colours.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds account scoping ('on this account') and the fact that results contain slugs usable as `brand` values, which is useful behavioral context beyond the 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 with no waste: the first states what the tool returns, and the second explains how to use a returned value. The most important information is front-loaded.
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, read-only list tool, the description is sufficiently complete: it names the resources, scopes them to the account, and shows how results are consumed downstream. It does not describe the exact response shape, but the simplicity of the tool makes that a minor gap.
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 has zero parameters, so parameter semantics is not a burden on the description. The description still meaningfully mentions the `brand` slug concept, which clarifies how the output relates to another tool's parameter.
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 title 'List brand kits' and description 'Saved brand palettes on this account' clearly identify a read/list operation over brand kit resources. It does not explicitly distinguish itself from list_palettes, but its relationship to generate_background via slug usage adds specificity.
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 description gives concrete downstream usage: pass a kit's slug as `brand` to generate_background to render on-brand without repeating hex colors. It implies this tool is useful for fetching those slugs but does not explicitly mention when to use list_brand_kits instead of list_palettes or other siblings.
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.