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suggest_theme

Generates a complete visual theme preset (colors, fonts, radii, shadows, spacing) with ready-to-use CSS variables and Tailwind config, based on app type, industry, mood, and dark mode preference.

Instructions

Sugere um preset de tema visual baseado no tipo de app. Retorna tokens completos: cores, fontes, raios, sombras, espaçamentos. Inclui CSS variables e config do Tailwind prontos para usar.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
moodNoMood visual: minimal | bold | editorial | data-heavy | friendly
appTypeNoTipo do app: dashboard | crm | blog | landing | saas | portfolio | ecommerce
darkModeNoSe o app tem dark mode como padrão
industryNoIndústria: tech | finance | health | education | creative | startup
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses the output format: complete tokens, CSS variables, and Tailwind config, giving the agent a clear expectation of what will be returned. It does not explicitly state side-effect-free behavior, but the constructive nature of 'suggest' implies it, and the output details add valuable context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences that state the purpose and output format without unnecessary detail. It is front-loaded with the main action and delivers the essential information in a compact manner.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema, the description adequately conveys what the agent will receive: tokens, CSS variables, and Tailwind config. It does not explain how parameters affect the output, but given all parameters are optional and the schema descriptions are clear, this is sufficient. A minor gap is not specifying default behavior when no parameters are provided, but this is not critical.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has descriptions for all four parameters (100% coverage), so the description does not need to repeat them. The description only mentions 'based on the type of app,' which aligns with the appType parameter but does not add relationships or usage details beyond the schema. Thus, it adds marginal value over the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool suggests a visual theme preset based on the app type, and specifies the output includes tokens (colors, fonts, radii, shadows, spacings) and CSS/Tailwind config. This distinguishes it from siblings like get_theme (retrieving a specific theme) and list_presets (listing existing presets), making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by mentioning 'based on the type of app,' giving context that this tool generates a suggestion when an app type is known. However, it does not explicitly mention when to use it over alternatives like get_theme or list_presets, nor does it provide any exclusions or alternative recommendations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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