suggest_backgroundsRank structures for a natural-language brief ("dark aurora glow for an AI landing page"). Returns why each matched, matching built-in palettes, and a ready-to-run example request for generate_background.
| Name | Required | Description | Default |
|---|---|---|---|
| brief | Yes | ||
| limit | No |
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 and idempotent behavior, and the description adds useful output behavior: it returns why each match occurred, matching built-in palettes, and an example request for generate_background. This goes beyond the annotations without contradicting them.
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?
The entire description is one front-loaded sentence that covers purpose, input style, return contents, and a pointer to generate_background. There is no filler or redundant repetition of the title or schema.
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?
With no output schema, the description appropriately lists the returned elements: why each matched, matching built-in palettes, and an example request. The only notable omission is any guidance on the 'limit' parameter, but the parameter name and schema constraints largely speak for themselves.
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 0%, so the description must add meaning. It provides a concrete example of what a natural-language 'brief' looks like, but it does not mention the 'limit' parameter at all. The brief semantics are improved, but limit remains only structurally defined.
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's verb and resource: 'Rank structures for a natural-language brief' and explains what it returns. It is distinguishable from siblings like generate_background and list_backgrounds because it emphasizes ranking and producing a ready-to-run generate_background example, though it does not explicitly contrast itself with those siblings.
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 clear context: this tool is for natural-language briefs and returns ranked suggestions rather than a single generated artifact. It does not explicitly say when not to use it or name alternative tools, but the example and output framing imply the appropriate use case.
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.