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brand_extract_messaging

Audit a brand's current voice on its website by analyzing formality, jargon, active voice, and messaging gaps. Returns structured scores and writes a messaging audit file.

Instructions

Audit how a brand currently sounds on its website — the first step in Session 3 (brand voice and messaging). Use when the user says 'analyze my voice', 'brand voice audit', 'how does my brand sound?', or 'start Session 3'. Analyzes voice fingerprint (formality, jargon density, active voice %, hedging), vocabulary frequency, claims quality, AI-ism detection, and messaging gaps. Writes .brand/messaging-audit.md. After this, run brand_compile_messaging to define how the brand should sound. Returns structured analysis with scores.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPrimary website URL to audit (typically the homepage, e.g. 'https://acme.com')
pagesNoJSON array of additional page URLs to include (e.g. '["https://acme.com/about", "https://acme.com/services"]'). Analyzes up to 10 pages.
Behavior4/5

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

With no annotations, the description effectively discloses key behaviors: it performs analysis, writes a file (.brand/messaging-audit.md), and returns structured scores. It avoids contradictions.

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?

The description is well-structured and front-loaded with the core purpose. It could be slightly more concise but every sentence adds useful information.

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 the tool's complexity (multi-faceted analysis, file output, no output schema), the description is thorough, covering inputs, process, output file, and next steps.

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 100%, and the description adds context like the url example and the limit of 10 additional pages. This adds value beyond the schema's basic descriptions.

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 audits a brand's voice on its website, analyzing specific elements like formality, jargon, and claims. It distinguishes itself from siblings by explicitly mentioning brand_compile_messaging as a subsequent step.

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?

It provides specific trigger phrases ('analyze my voice', 'brand voice audit') and indicates when to use it. However, it does not explicitly state when not to use it or list alternative tools for similar tasks.

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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