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Brand-System

@brandsystem/mcp

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brand_extract_web

Extract brand colors, fonts, and logo from any website URL by parsing its HTML and CSS. Returns confidence-scored results including color roles, font frequencies, and logo preview.

Instructions

Extract brand colors, fonts, and logo from any website URL — get brand identity from a live site. Use when asked 'extract brand from URL', 'get brand colors from website', 'scan my site', or when the user provides a website URL. Parses HTML for logo candidates (SVG, img, favicons, Clearbit fallback) and CSS for colors and font-family declarations. Confidence-scores everything. Pass logo_url to fetch a specific logo directly. Returns colors with roles, fonts with frequency, logo preview data, and extraction quality score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesWebsite URL to scan (e.g. 'https://acme.com'). The homepage usually has the best logo and color data.
logo_urlNoDirect URL to a logo SVG/PNG file (e.g. 'https://acme.com/logo.svg'). Use if automatic extraction misses the logo.
Behavior4/5

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

With no annotations, the description carries the full burden. It explains the extraction process (HTML parsing for logo candidates, CSS for colors/fonts), confidence-scoring, and the optional logo_url parameter. This provides adequate behavioral insight.

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 front-loaded with the core purpose and includes all necessary details. It is slightly verbose but each sentence adds value. Could be tightened slightly, but overall efficient.

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?

Given no output schema, the description appropriately outlines return values (colors with roles, fonts, logo preview, quality score). It covers extraction behavior and parameter usage well, though it omits error handling or rate limits.

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%, so baseline is 3. The description adds value by explaining that the homepage usually has the best data for url, and that logo_url is useful if automatic extraction fails. This goes 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 extracts brand colors, fonts, and logo from any website URL. It specifies the action, resource, and scope, and distinguishes from siblings like brand_extract_figma and brand_extract_pdf by focusing on live websites.

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 explicitly lists user queries that trigger this tool (e.g., 'extract brand from URL', 'get brand colors from website'), providing clear usage context. It does not mention when not to use, but the guidance is sufficient.

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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