brandsystem-mcp

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

No annotations are provided, so the description carries the full burden. It discloses behavioral traits such as parsing HTML for logos and CSS for colors/fonts, using Clearbit fallback, confidence-scoring, and returning structured data (colors with roles, fonts with frequency, etc.). However, it lacks details on error handling, rate limits, or authentication needs, which are important for a web-scraping tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, starting with the core purpose and usage guidelines, followed by implementation details and return values. Every sentence adds value, with no redundant or vague information, making it efficient and easy to parse.

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

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

Given the tool's complexity (web extraction with multiple data types), no annotations, and no output schema, the description does a good job explaining what the tool does, how it works, and what it returns. However, it could be more complete by detailing error cases or output structure more explicitly, as there is no output schema to rely on.

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

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

Schema description coverage is 100%, so the schema already documents both parameters. The description adds value by explaining the purpose of 'logo_url' ('Use if automatic extraction misses the logo') and recommending the homepage for 'url', providing context beyond the schema's basic descriptions. With high schema coverage, the baseline is 3, but the added guidance justifies a higher score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('extract', 'get', 'scan', 'parses') and resources ('brand colors, fonts, and logo', 'website URL', 'HTML', 'CSS'). It distinguishes itself from siblings by focusing on web extraction rather than Figma, PDF, or other sources mentioned in sibling tool names like 'brand_extract_figma' and 'brand_extract_pdf'.

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

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

The description provides explicit guidance on when to use the tool, listing specific user queries ('extract brand from URL', 'get brand colors from website', 'scan my site') and contexts ('when the user provides a website URL'). It also distinguishes usage by specifying the homepage as the best source for data, though it does not explicitly state when not to use it or name alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.