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brand_build_matrix

Generate messaging variants for every persona and buying stage. View the matrix as a grid or edit specific variants by ID to refine core messages.

Instructions

Generate persona x journey stage messaging variants — adapted core messages for every audience at every buying stage. Mode 'generate' creates variants using persona tensions, stage mindsets, and brand perspective. Mode 'view' shows the matrix as a grid. Mode 'edit' refines a specific variant by ID. Requires personas and journey stages in strategy.yaml. Returns variant grid with status tracking (Draft/Active/Retired).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo'generate' creates messaging variants for every persona × stage; 'view' returns the matrix as a grid; 'edit' updates a specific variant by IDgenerate
variant_idNoID of the variant to edit (required for mode='edit', e.g. MV-001)
answersNoJSON string with variant fields to update: core_message, tone_shift, proof_points, status (for mode='edit')
Behavior4/5

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

The description discloses all three modes' behaviors and the return format ('Returns variant grid with status tracking (Draft/Active/Retired)'). With no annotations, it covers safety (read/edit/generate) and dependencies, though it could mention persistence for edits.

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 concise with 5 sentences, each serving a clear purpose: mode overview, three mode-specific sentences, dependency, and output description. No redundancy or unnecessary details.

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

Completeness5/5

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

Given 3 parameters, no output schema, and no annotations, the description fully equips an AI agent: it covers all modes, prerequisites, return value with status tracking, and parameter specifics. No gaps remain for correct invocation.

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

Parameters5/5

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

Schema coverage is 100%, and the description adds significant meaning: it explains the purpose of each mode, the format of 'answers' JSON, and the prerequisite. It also describes the output format, which is absent from the schema, providing critical context for invocation.

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 states exactly what the tool does: 'Generate persona x journey stage messaging variants' with three clear modes. It distinguishes from siblings like brand_build_personas and brand_build_themes by focusing on the matrix generation across persona and stage.

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 explains each mode's purpose and the prerequisite ('Requires personas and journey stages in strategy.yaml'). It implies when to use each mode (generate, view, edit) but does not explicitly state when not to use it or mention alternatives, which is less critical given the distinct sibling set.

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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