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Update brand context

update_brand

Update your project's brand identity with selective field merging. Modify voice, audience, tone, colors, fonts, logo, and languages while preserving unchanged values.

Instructions

Patch the project's brand: voice, audience, tone, colors, fonts, logo, languages. Only the fields you pass are updated; others are preserved (merge semantics). Free; doesn't consume credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fontsNo
voiceNo
colorsNo
audienceNo
logo_urlNo
descriptionNoReads from / writes to TenantHomepage.company_name.
content_toneNo
content_goalsNo
default_languageNo
preferred_topicsNo
target_languagesNo
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses merge semantics and that the operation is free (doesn't consume credits). However, it does not mention permissions, persistence guarantees, error behavior, or the response shape, which are important for a mutation tool.

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 sentences with no filler. It front-loads the action and resource, states merge behavior, and adds a valuable cost note. Every sentence earns its place.

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

Completeness3/5

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

For an 11-parameter tool with no output schema and sparse schema descriptions, the description covers core usage (patch, merge, free) and names major fields. It is enough to select the tool, but not fully sufficient to understand nested structures, language codes, or all field semantics without additional schema inspection.

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

Parameters2/5

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

Input schema coverage is only 9%, so the description must compensate, but it only lists field categories rather than explaining the 11 parameters. It also loosely maps 'tone' to content_tone, 'logo' to logo_url, and 'languages' to target_languages/default_language, which may require inference. Merge semantics helps, but individual parameter details remain undocumented.

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 uses a specific verb ('Patch') and clearly identifies the resource ('the project's brand') while enumerating the affected fields (voice, audience, tone, colors, fonts, logo, languages). This distinguishes it from sibling tools like get_brand and other brand/content operations.

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

Usage Guidelines4/5

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

The description clearly conveys that this tool is for partial updates with merge semantics ('Only the fields you pass are updated; others are preserved'), which is strong usage context. However, it does not explicitly name alternatives or state when to avoid using this tool, so it lacks exclusions.

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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