Figma MCP Server: 47 AI Tools for Design Files, Components, Variables & Collaboration
Figma is where product teams live — design files, component libraries, design tokens, comments, activity logs. But AI agents can't see inside Figma unless you give them structured access to the right data at the right granularity.
MCP (Model Context Protocol) solves this by letting AI agents call Figma as a set of typed tools. Instead of pasting screenshots into ChatGPT or manually describing your component hierarchy, the AI reads your files, inspects your components, checks your variables, and posts comments — all programmatically.
There are two ways to connect Figma via MCP: Figma's official MCP server and MCPBundles' 47-tool REST API bundle. This guide covers both.
Two Ways to Connect
1. Figma's Official MCP Server
Figma launched their own MCP server at https://mcp.figma.com/mcp. It uses OAuth 2.1 with the mcp:connect scope and exposes Figma's capabilities through dynamically discovered tools.
Pros: First-party support, maintained by Figma, OAuth-based auth. Cons: Tool surface is defined by Figma — you get what they expose.
On MCPBundles, this is available as the Figma MCP bundle (figma-mcp). Connect with OAuth or a personal access token, and the tools are discovered automatically from Figma's server.
2. MCPBundles REST API Bundle — 47 Tools
MCPBundles also wraps Figma's full REST API into 47 structured MCP tools organized around real design workflows. This gives you granular control over every Figma API surface — files, nodes, components, styles, variables, comments, webhooks, dev resources, activity logs, payments, and library analytics.
Pros: Full API coverage, every tool individually callable, structured parameters with validation. Cons: REST-based (not the official MCP transport).
Both bundles are available on MCPBundles — pick the one that fits your workflow, or enable both.
What AI Agents Can Do With Figma
Access and Explore Design Files
The foundation of any Figma integration is reading design files. AI agents can pull file metadata, navigate the node tree, and export images — all without opening the Figma UI.
Tools: figma_get_file, figma_get_file_nodes, figma_get_images, figma_get_file_meta, figma_get_file_versions, figma_get_image_fills
| What you ask the AI | What happens |
|---|---|
| "Show me the structure of this Figma file" | Reads the file's document tree — pages, frames, groups, layers |
| "Export the hero section as PNG" | Gets the node ID from the tree, calls image export with format and scale |
| "What changed in the last 3 versions?" | Pulls version history with timestamps and descriptions |
| "Get all image fills in this file" | Returns download URLs for every embedded image |
Key detail: The file key comes from the Figma URL — figma.com/file/ABC123/Name → key is ABC123. Node IDs come from the document tree. The AI learns this pattern once and reuses it across all file operations.
Work With Components and Styles
Component libraries are the backbone of design systems. AI agents can inspect individual components, browse team-level libraries, and read style definitions — making design system audits and documentation automatic.
Tools: figma_get_component, figma_get_team_components, figma_get_file_components, figma_get_component_set, figma_get_team_component_sets, figma_get_file_component_sets, figma_get_style, figma_get_team_styles, figma_get_file_styles
| What you ask the AI | What happens |
|---|---|
| "List all components in our design system file" | Returns every component with name, description, and metadata |
| "Find all buttons in the team library" | Searches team components, returns component sets with variants |
| "What text styles are defined in this file?" | Returns all text styles with font family, size, weight, color |
| "Document our color palette" | Reads all color styles and generates a structured palette |
Example workflow: "Audit our design system" → List team components → Check for missing descriptions → List styles → Cross-reference with code variables → Generate documentation.
Manage Design Variables (Design Tokens)
Figma Variables are design tokens — colors, spacing, typography values that drive your entire design system. AI agents can read local and published variables, and even create or update them programmatically.
Tools: figma_get_local_variables, figma_get_published_variables, figma_post_variables
| What you ask the AI | What happens |
|---|---|
| "What spacing tokens are defined?" | Reads all local variables, filters by collection |
| "Sync design tokens from our published library" | Pulls published variables with their resolved values |
| "Add a new color variable called brand-accent" | Creates the variable with the specified value and collection |
| "Compare local vs published variables" | Reads both sets and highlights differences |
Why this matters: Design tokens are the bridge between design and code. AI agents that can read and write Figma Variables can keep design systems in sync, flag drift between local and published tokens, and automate token migration across files.
Collaborate via Comments
Design reviews happen in comments. AI agents can read, post, and manage comments and reactions — enabling automated review workflows, status updates, and feedback collection.
Tools: figma_get_comments, figma_post_comment, figma_delete_comment, figma_get_comment_reactions, figma_post_comment_reaction, figma_delete_comment_reaction
| What you ask the AI | What happens |
|---|---|
| "Show me all unresolved comments on this file" | Returns comments with author, timestamp, position, and resolved status |
| "Post a review comment on the header frame" | Creates a comment pinned to a specific node |
| "React with a thumbs-up to all resolved comments" | Reads comments, filters resolved, adds reactions |
Monitor Team Activity and Analytics
For design ops teams, understanding how your component library is used across the organization is critical. AI agents can pull activity logs and library analytics to surface adoption metrics and usage patterns.
Tools: figma_get_activity_logs, figma_get_library_analytics_component_actions, figma_get_library_analytics_component_usages, figma_get_library_analytics_style_actions, figma_get_library_analytics_style_usages, figma_get_library_analytics_variable_actions, figma_get_library_analytics_variable_usages
| What you ask the AI | What happens |
|---|---|
| "Which components are most used across the org?" | Pulls library analytics for component usages |
| "Show me component insertion activity this month" | Returns component actions (inserts, detaches) with timestamps |
| "Are there any styles nobody is using?" | Cross-references style definitions with usage analytics |
| "What happened in our team's Figma activity today?" | Returns activity logs with events, actors, and timestamps |
Automation Workflows
Design System Documentation
Workflow: Read components → Read variables → Read styles → Generate docs
figma_get_team_componentsto list all components with descriptionsfigma_get_published_variablesto get all design tokensfigma_get_team_stylesto get color, text, and effect styles- AI generates structured documentation from the data
Design Review Automation
Workflow: Read comments → Analyze feedback → Post summary → Track resolution
figma_get_commentsto pull all comments on a file- AI categorizes feedback (bug, suggestion, question, approval)
figma_post_commentto post a summary of open items- Track resolution status over time
Design-to-Code Sync
Workflow: Read variables → Compare with code tokens → Flag drift
figma_get_local_variablesto get current design tokens- Compare against your codebase token definitions
- Flag mismatches — new tokens, changed values, missing tokens
figma_post_variablesto update Figma if code is the source of truth
File Export Pipeline
Workflow: Read file → Identify assets → Export at multiple scales
figma_get_fileto read the document tree- Identify exportable nodes (icons, illustrations, photos)
figma_get_imagesto export each node at 1x, 2x, 3x scales- Download and organize by category
Connecting Figma
Authentication
Personal Access Token (quickest):
Generate a token at figma.com/developers/api → Settings → Personal Access Tokens. Add it as a credential on MCPBundles.
OAuth 2.0 (team-wide): Figma supports OAuth with scopes for files, comments, dev resources, and webhooks. MCPBundles handles the OAuth flow — connect once, and the token refreshes automatically.
Getting Started
- Enable the Figma bundle on MCPBundles
- Add your personal access token or connect via OAuth
- Ask your AI agent: "List all components in my design system file"
The AI has access to all 47 tools and will pick the right ones based on what you ask.
Frequently Asked Questions
Q: Which Figma bundle should I use — Figma MCP or Figma (REST)?
If you want Figma's officially supported MCP server, use Figma MCP (figma-mcp). If you want full REST API coverage with 47 individually callable tools, use Figma (figma). You can enable both — they use separate credentials.
Q: Do I need a paid Figma plan?
The REST API works with any Figma plan. Some features (library analytics, activity logs, published variables) require a Figma Organization or Enterprise plan. The tools will return appropriate errors if your plan doesn't include a feature.
Q: How do I find a file's key?
From the URL: figma.com/file/ABC123/My-Design → the file key is ABC123. The AI learns this pattern and extracts it automatically.
Q: Can AI agents modify my designs?
The current tool set is primarily read-oriented. Write operations are available for comments (post/delete), comment reactions, variables (create/update), dev resources, and webhooks. The AI cannot directly modify design elements like shapes or text on the canvas.
Q: What about rate limits?
Figma's API rate limit is roughly 100 requests per minute. The tools handle rate limiting gracefully — if you hit the limit, the error message tells the AI to wait and retry.
Ready to connect Figma to your AI workflow? Enable the Figma bundle and start exploring your design files, components, and tokens with AI — no Figma UI required.