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Update brand voice

update_brand_voice

Change caption tone by updating only the brand voice fields you specify; other settings stay untouched. Fails if the @handle is taken, suggesting an alternative.

Instructions

Change how captions sound. Only the fields you send are changed; the rest are left alone. The @handle is globally unique — if it is taken the call fails with a suggestion you can offer the user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nicheNoWhat the brand sells or is about.
brandNameNoName captions should call the brand.
brandHandleNoPublic @handle, without the @. Globally unique across all accounts.
workspaceIdNoWorkspace to act in. Defaults to the one set by use_workspace, or the account's first workspace. Pass this to act on a different workspace for one call only.
mentionBrandNoWhether captions should name the brand at all.
defaultPlatformNoPlatform captions are written for by default.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior4/5

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

Annotations declare a non-destructive write (readOnlyHint=false, destructiveHint=false, openWorldHint=true), and the description adds real value beyond them: explicit partial-update/PATCH semantics ("only the fields you send are changed") and a documented failure mode for the globally unique @handle with a suggested value returned. It stops short of stating permission requirements or whether changes are reversible.

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

Conciseness4/5

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

Three short sentences, no filler, with the core action stated first and the failure behavior last. Sizing is appropriate for a six-parameter optional-field tool.

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 a write tool with no output schema and all-optional parameters, the description covers patch semantics and one failure mode but omits what the call returns, whether a partially-sent update can clear a field, and any permission context. Adequate, with visible gaps.

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?

Schema description coverage is 100%, so all six parameters are already documented in the schema, which sets the baseline at 3. The description reinforces the handle-uniqueness constraint and the partial-update contract but adds no field-level syntax or format beyond what the schema provides.

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

Purpose3/5

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

"Change how captions sound" conveys a mutation of caption/brand styling but stays metaphorical and never says it edits the brand voice configuration (niche, brand name, handle, platform). The name and title carry more specificity than the description does, so an agent gets only an approximate sense of purpose.

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

Usage Guidelines2/5

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

There is no guidance on when to use this versus reading settings first (e.g., get_brand), no prerequisites, and no mention of when not to call it. The only implied usage is "change something," leaving routing entirely to inference.

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