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TranHoaiHung

figma-ui-mcp

by TranHoaiHung

figma_rules

Generates a design system rule sheet from the current Figma file, aggregating color tokens, typography styles, variables, and components into a single markdown block for AI context.

Instructions

Generate a design system rule sheet from the current Figma file — aggregates color tokens, typography styles, variables (all modes), and component catalog into a single markdown block. Equivalent to official Figma MCP's create_design_system_rules. Call once at the start of a design-to-code session to give the AI full context: what tokens to use, what text styles exist, which components are available. Re-run when the design system changes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sessionIdNoTarget a specific Figma file/tab. Omit to auto-select.
Behavior4/5

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

With no annotations, the description carries full burden. It clearly describes the tool as aggregating multiple design elements into a markdown block without side effects, implying read-only. It could add details about authentication or performance, but the behavior is well communicated.

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?

The description is roughly 80 words, front-loaded with the primary action, then content details and usage guidance. Every sentence adds value with no fluff, achieving excellent conciseness.

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 no output schema, the description fully explains the output format (single markdown block) and contents (tokens, styles, variables, components). It provides complete context for when and why to use the tool, making it self-sufficient for an AI agent.

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

Parameters3/5

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

Input schema has 100% description coverage with a single optional sessionId parameter. The description does not add any additional meaning beyond what the schema already provides, maintaining baseline.

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 generates a design system rule sheet aggregating color tokens, typography styles, variables, and component catalog into markdown. It uses specific verb 'generate' and resource, and distinguishes indirectly by mentioning equivalence to official MCP and usage context.

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?

Explicitly states when to call: 'once at the start of a design-to-code session' and 're-run when the design system changes.' It also explains what context it provides. However, it does not explicitly exclude use cases where sibling tools like figma_read might be more appropriate.

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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