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brand_runtime

Read compiled brand runtime to provide brand system context for AI agents. Supports slice options to optimize token usage.

Instructions

Read the compiled brand runtime contract (brand-runtime.json). Returns the brand system that AI agents load as context for on-brand output. Supports slicing: 'full' (~1200 tokens, everything), 'visual' (~200 tokens, colors + fonts + anti-patterns), 'voice' (~400 tokens, tone + vocabulary + perspective), 'minimal' (~100 tokens, primary color + heading font). Use slices when passing brand context to sub-agents — smaller context reduces token cost and agent satisficing. Live Mode aware: when enabled via brand_brandcode_live, the runtime refreshes from the hosted Brandcode runtime on each call (subject to cache TTL). Falls back silently to the local mirror on network error. Read-only. Run brand_compile to refresh.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sliceNoWhich slice of the runtime to return. 'full': everything (~1200 tokens). 'visual': colors, typography, logo, anti-patterns, composition (~200 tokens). 'voice': tone, vocabulary, never-say, perspective (~400 tokens). 'minimal': primary color, heading font, logo reference only (~100 tokens). Use slices to reduce prompt size when handing off to sub-agents that only need part of the brand.full
Behavior5/5

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

No annotations provided, so description carries full burden. Describes read-only behavior, live mode refresh behavior, fallback on network error, and token counts for slices. No contradictions.

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?

Every sentence adds unique information: purpose, return value, slice usage, live mode, fallback, read-only. No redundancy, well-structured and front-loaded.

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?

Fully covers tool behavior for its complexity: one parameter with sufficient detail, return value explained, edge cases (fallback) covered, no output schema needed.

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 already has 100% coverage with enum and description. Description adds value by specifying actual token counts and practical guidance for slice selection, exceeding baseline 3.

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?

Clear verb ('Read') and resource ('compiled brand runtime contract'). Explicitly states it returns the brand system for on-brand output. Differentiation from sibling tool 'brand_compile' (which refreshes) is clear.

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?

Provides explicit guidance on using slices for sub-agents to reduce token cost and satisficing. Mentions live mode awareness and silent fallback. Lacks explicit when-not-to-use, but context provides implicit guidance.

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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