| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Figma's document hierarchy as queryable data structures through MCP tools, allowing clients to recursively traverse frames, components, groups, and design tokens without manual API pagination. Implements a local caching layer that mirrors the Figma REST API response structure, enabling fast repeated access to design system metadata without rate-limit pressure on Figma's servers.
Unique: Implements MCP as a bridge between Figma's REST API and LLM clients, caching the full document tree locally to avoid repeated API calls and enabling stateless tool invocations from Claude/Gemini without managing session state
vs alternatives: Unlike direct Figma API clients, this MCP server abstracts authentication and pagination, allowing AI agents to query design files with simple tool calls while respecting Figma's rate limits through local caching
Automatically discovers and catalogs all component variants within a Figma file, extracting variant properties (color, size, state) and their corresponding design tokens. Uses Figma's component set structure to build a queryable registry that maps variant combinations to visual properties, enabling code generators to understand design system constraints and generate type-safe component APIs.
Unique: Parses Figma's component variant naming syntax to automatically extract property dimensions and values, then maps these to design tokens, enabling bidirectional sync between design and code without manual configuration
vs alternatives: More comprehensive than Figma's native variant export because it builds a queryable registry with token mappings, allowing AI agents to reason about variant coverage and generate exhaustive component tests
Extracts design tokens (colors, typography, spacing, shadows) from Figma's native token system or from component properties, normalizing them into a standardized JSON format compatible with design token standards (W3C Design Tokens, Tokens Studio). Implements token aliasing and hierarchical organization to map Figma's visual properties to semantic token names usable in code.
Unique: Implements token normalization that converts Figma's native token format into W3C-compliant JSON, preserving semantic relationships and enabling downstream tooling (Tokens Studio, Style Dictionary) to consume the output without custom parsing
vs alternatives: Unlike manual token export or Figma plugins that generate CSS, this MCP server produces portable JSON that works with any design token framework and integrates seamlessly with AI agents that need to reason about design constraints
Exports individual Figma frames or artboards as structured data including layout information, child elements, text content, and visual properties. Implements a recursive export strategy that preserves the design hierarchy while flattening it into queryable JSON, enabling code generators to understand page structure and generate corresponding HTML/React layouts.
Unique: Preserves Figma's hierarchical structure in JSON while flattening it for code generation, including auto-layout metadata that enables downstream tools to infer responsive behavior without manual layout interpretation
vs alternatives: More structured than screenshot-based design-to-code because it exports semantic layout information, allowing AI agents to generate semantically correct HTML rather than pixel-based approximations
Implements the Model Context Protocol server interface, automatically registering Figma operations as callable tools with JSON Schema definitions. Handles request/response serialization, error handling, and tool discovery, allowing Claude, Gemini, and other MCP-compatible clients to invoke Figma operations as first-class functions without custom integration code.
Unique: Implements the full MCP server lifecycle (initialization, tool registration, request handling, error propagation), abstracting the protocol complexity so Figma operations appear as native tools to LLM clients without custom middleware
vs alternatives: Unlike REST API wrappers or custom integrations, MCP server registration enables seamless tool discovery and invocation in Claude Desktop and Cursor, reducing friction for non-technical users to access Figma programmatically
Maintains a local in-memory cache of Figma document structure and metadata, populated at server startup from the Figma API. Enables repeated queries without hitting Figma's rate limits and provides offline access to cached data after initial sync. Implements cache invalidation strategies (TTL, manual refresh) to balance freshness with performance.
Unique: Implements a simple in-memory cache that mirrors Figma's API response structure, allowing clients to query cached data without pagination or authentication overhead while maintaining API token security on the server
vs alternatives: More efficient than repeated API calls for high-frequency queries, but less sophisticated than distributed caching systems — suitable for single-server deployments where cache consistency is not critical
Provides native integration with Cursor IDE and Claude Desktop through MCP protocol, enabling users to invoke Figma queries directly from the editor or chat interface. Implements context injection that allows Figma data to be referenced in code generation prompts, and supports tool invocation from natural language queries without explicit API calls.
Unique: Bridges the gap between design and code by making Figma a first-class data source in Cursor and Claude Desktop, allowing developers to reference design context in code generation without context switching to Figma
vs alternatives: Unlike manual design-to-code workflows or separate design tools, this integration embeds Figma queries directly in the IDE, reducing friction and enabling AI-assisted code generation that respects design constraints
Exposes Figma operations as command-line tools accessible through the Gemini CLI, enabling shell scripts and CI/CD pipelines to query Figma programmatically. Implements tool invocation through standard input/output, allowing Figma data to be piped into other CLI tools for automated design system workflows.
Unique: Exposes MCP tools through Gemini CLI's command-line interface, enabling shell-based automation and CI/CD integration without custom scripting or API client libraries
vs alternatives: More scriptable than GUI-based Figma access, and more flexible than Figma's native webhooks because it allows on-demand queries rather than event-driven updates
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs figma-mcp-server at 27/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities