brandsystem-mcp

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the two-phase workflow, the override behavior ('Figma-sourced values override web-extracted values'), and the output characteristics ('Returns merged identity data with high-confidence scores'). However, it doesn't mention potential limitations like file size constraints, authentication requirements, or error conditions.

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 efficiently structured in three sentences that each earn their place: first states the core purpose and advantage, second explains the workflow, third provides usage context and output. No wasted words, and key information is front-loaded about what the tool does and why it's valuable.

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?

For a complex 5-parameter tool with no annotations and no output schema, the description does well in explaining the workflow, usage context, and output characteristics. However, it could provide more detail about what 'merged identity data' contains or how confidence scores are calculated. The absence of an output schema means the description should ideally explain return values more thoroughly.

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 all parameters thoroughly. The description adds some context about the two-phase workflow corresponding to the 'mode' parameter and mentions the types of data processed (variables, styles, logo_svg), but doesn't provide additional semantic meaning beyond what's in the schema descriptions. This meets the baseline for high schema coverage.

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 brand identity') and resources ('Figma design file'), listing the exact elements extracted (colors, typography, logo). It distinguishes from sibling tools like 'brand_extract_web' by specifying 'higher accuracy than web extraction' and 'Figma-sourced values override web-extracted values', making its unique value proposition explicit.

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 this tool ('Use when the user has a Figma file URL or key') and outlines a clear two-phase workflow with specific modes ('plan' and 'ingest'). It implicitly distinguishes from web extraction tools by stating Figma provides higher accuracy, though it doesn't name specific sibling alternatives beyond the general comparison.

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