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F-MCP (Figma MCP Bridge)

by atezer

figma_import_style

Destructive

Import a published text, paint, or effect style from a team library by key, and optionally apply it to a selected node.

Instructions

Import a text, paint, or effect style from a team library by key, and optionally apply it to a node. IMPORTANT: This API only imports PUBLISHED LIBRARY styles, NOT local file styles. For local styles, use 'node.fillStyleId = style.id' (or textStyleId/effectStyleId) directly via figma_execute. Get library style keys from .claude/libraries/ cache or REST API: figma_rest_api GET /v1/files/{fileKey}/styles. For TEXT styles: applies via setTextStyleIdAsync (includes font, size, weight). For PAINT styles: applies via fillStyleId. For EFFECT styles: applies via effectStyleId.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
figmaUrlNoFigma file URL for routing.
fileKeyNoTarget a specific connected file.
styleKeyYesLibrary style key (must be from a PUBLISHED team library, not a local style)
nodeIdNoNode ID to apply the style to (optional — omit to just import)
Behavior4/5

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

Annotations already mark destructiveHint=true, so the bar is lower. The description adds behavioral details: it imports from published libraries, and for different style types it applies via specific methods (setTextStyleIdAsync, fillStyleId, effectStyleId). It does not elaborate on side effects or reversibility, but adds useful context beyond the annotation.

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 structured with a clear purpose sentence, an important note, guidance on obtaining keys, and a breakdown by style type. It is informative but somewhat lengthy; however, every sentence adds value and the structure is logical.

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

Completeness3/5

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

Given no output schema, the description does not explain the return value or success/failure indicators. It covers the tool's purpose, usage constraints, and parameter details well, but lacks information on what the tool outputs, which is important for an agent to handle the result.

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% with descriptions. The description adds meaning beyond the schema by explaining how to obtain styleKey (from cache or REST API) and detailing how each style type is applied. This provides actionable context for correct parameter usage.

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 verb 'import' and the resource 'style from a team library', with optional application to a node. It distinguishes from siblings like figma_get_styles by focusing on importing and applying from published libraries.

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

Usage Guidelines5/5

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

Explicitly states that this tool only works with published library styles, not local file styles, and provides alternative methods for local styles via figma_execute. Also guides on obtaining style keys from cache or REST API, giving clear when-to-use and when-not-to-use 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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