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Hermoso

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by hermoso-ai
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JavaScript
Remote

Update profile fields

update_brand
Destructive

Patch specific fields of the active brand profile without overwriting the rest. Use it to update voice, audience, positioning, or product pronunciations.

Instructions

Patch SPECIFIC fields of the active profile (name, domain, sells, summary, category, audience, positioning, voice, style, goal, and how the brand and product names are pronounced) WITHOUT overwriting the rest — a read-modify-write on the saved brand. Use for “change our voice to playful”, “we sell to dentists now”. To onboard a brand from scratch, use draft_brand. If no brand is saved yet and you only INFERRED one from what the user is making, ask them to confirm it is their brand before saving it (they may be working for a client or just trying things). Only pass the fields you’re changing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalNocurrent marketing goal
nameNo
brandNoprofile id/name from list_brands, this call only
sellsNowhat the brand sells
styleNovisual style — palette, typography, aesthetic
voiceNobrand voice/tone
domainNowebsite domain
summaryNoone-line description
audienceNo
categoryNo
pronounceNohow the brand NAME is said aloud, as a simple respelling with the stressed syllable in capitals (e.g. "KOH-dee-ak"). Videos use it as a delivery note beside the spoken line; set it when a render mispronounced the name.
positioningNo
pronunciationsNohow PRODUCT names are said aloud, e.g. {"Power Cakes": "POW-er cakes"}. Merged into the saved ones; an empty string removes one.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.374
    • changedInput schema / properties / brand / description
      Previous value: -"brand id/name from list_brands, this call only"New value: +"profile id/name from list_brands, this call only"
  2. Changed1 schema field changedv0.1.371
    • addedInput schema / properties / brand
      Added value: +{
+  "description": "brand id/name from list_brands, this call only",
+  "type": "string"
+}
  3. Changed2 schema fields changedv0.1.366
    • addedInput schema / properties / pronounce
      Added value: +{
+  "description": "how the brand NAME is said aloud, as a simple respelling with the stressed syllable in capitals (e.g. \"KOH-dee-ak\"). Videos use it as a delivery note beside the spoken line; set it when a render mispronounced the name.",
+  "type": "string"
+}
    • addedInput schema / properties / pronunciations
      Added value: +{
+  "additionalProperties": {
+    "type": "string"
+  },
+  "description": "how PRODUCT names are said aloud, e.g. {\"Power Cakes\": \"POW-er cakes\"}. Merged into the saved ones; an empty string removes one.",
+  "propertyNames": {
+    "type": "string"
+  },
+  "type": "object"
+}
  4. Addedv0.1.161

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true, idempotentHint=false, and readOnlyHint=false, so the safety profile is covered. The description adds real context beyond that: read-modify-write semantics, that unspecified fields are preserved, and a confirmation requirement before saving an inferred brand. It stops short of describing the return or error behavior, but the added patch semantics are substantive.

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?

Front-loaded with the essential semantic (patch specific fields without overwriting), followed by examples, the alternative tool, and an edge-case rule. The long parenthetical field list and the confirm-your-brand aside make it denser than necessary, but every sentence carries actionable information.

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 13-param mutation tool with no output schema, the description covers the mutation model, field scope, alternative tool, and an ambiguous-inferred-brand workflow. What it lacks — success/return shape and permission requirements — is partly absorbed by the annotations, so it is close to complete.

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

Parameters4/5

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

Schema coverage is 69% across 13 params, so some fields (name, audience, category, positioning) are undocumented in the schema. The description compensates by enumerating the patchable fields and, crucially, adds the directive 'Only pass the fields you're changing' — meaningful guidance the schema does not provide for a 0-required-param patch tool.

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?

States a precise verb+resource+scope: 'Patch SPECIFIC fields of the active profile' with an explicit non-overwrite guarantee. It names the sibling it is not (draft_brand) and even names the fields covered, so an agent can distinguish it from create_brand/draft_brand without opening a schema.

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

Usage Guidelines5/5

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

Gives explicit when-to-use examples ('change our voice to playful', 'we sell to dentists now'), an explicit alternative for the other case ('To onboard a brand from scratch, use draft_brand'), and a conditional workflow rule for inferred brands. Both the positive and negative selection conditions are stated.

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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