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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Figma's document hierarchy (pages, frames, components, layers) as MCP resources that LLM agents can query and navigate. Implements a resource-based protocol where each Figma node becomes an addressable entity with metadata (type, name, bounds, properties), enabling agents to understand design structure without direct API calls. Uses MCP's resource subscription pattern to maintain live references to Figma objects.
Unique: Bridges Figma's REST API into MCP's resource protocol, allowing LLM agents to treat design files as queryable knowledge bases rather than opaque blobs. Implements lazy-loading of node metadata to handle large files efficiently.
vs alternatives: Unlike direct Figma API clients, this exposes design structure through MCP's standardized resource interface, enabling any MCP-compatible agent (Claude, custom LLMs) to introspect Figma without custom SDK integration.
Enables LLM agents to analyze Figma design elements (frames, components, text, shapes) and generate corresponding code (HTML/CSS, React, Vue, or other frameworks). The MCP server provides design metadata to the LLM, which uses chain-of-thought reasoning to map visual properties (layout, colors, typography, spacing) to code constructs. Supports component-aware generation where Figma components map to reusable code components.
Unique: Leverages MCP's resource protocol to feed Figma design metadata directly into LLM context, enabling multi-turn reasoning about design-to-code mapping without requiring custom Figma plugin development. Supports component-aware generation where Figma component hierarchies inform code structure.
vs alternatives: More flexible than rule-based design-to-code tools (Penpot, Anima) because it uses LLM reasoning to handle design variations; more maintainable than custom Figma plugins because it's framework-agnostic and updatable without Figma plugin deployment.
Exposes Figma API operations (create/update/delete nodes, modify properties, manage components) as MCP tools that LLM agents can invoke with structured arguments. Implements schema-based tool definitions where each Figma operation (e.g., 'update node fill color', 'create frame') is a callable tool with input validation, error handling, and response normalization. Handles authentication and API rate limiting transparently.
Unique: Wraps Figma's REST API as MCP tools with schema validation and error recovery, allowing LLM agents to perform mutations without custom API client code. Implements intelligent batching and rate-limit handling to work within Figma's API constraints.
vs alternatives: Simpler than building custom Figma plugins because it uses standard MCP tool protocol; more reliable than direct API calls from LLMs because it includes validation, error handling, and rate-limit management built-in.
Automatically extracts design tokens (colors, typography, spacing, shadows) from Figma styles and variables, normalizing them into structured formats (JSON, CSS variables, Tailwind config). Implements a mapping layer that translates Figma's style hierarchy into token definitions, with support for semantic naming (e.g., 'primary-button-color' instead of 'color-blue-500'). Enables bidirectional sync where token changes in Figma propagate to code.
Unique: Implements semantic token naming inference by analyzing Figma style hierarchies and usage patterns, producing human-readable token names rather than raw style IDs. Supports multiple output formats (JSON, CSS, Tailwind) from a single Figma source.
vs alternatives: More flexible than Figma's native token export because it supports multiple output formats and semantic naming; more maintainable than manual token extraction because it's automated and reproducible.
Analyzes Figma component hierarchies to identify component instances, overrides, and dependencies. Builds a dependency graph showing which components use which other components, enabling impact analysis for changes. Detects orphaned components, unused variants, and inconsistent overrides. Provides LLM agents with structured component metadata to reason about design system health.
Unique: Builds a queryable dependency graph from Figma component hierarchies, enabling LLM agents to reason about component relationships and impact of changes. Implements heuristic-based orphaned component detection to identify unused design system artifacts.
vs alternatives: More comprehensive than manual component audits because it's automated; more actionable than raw Figma API responses because it synthesizes dependency information into structured insights.
Enables LLM agents to add comments, annotations, and feedback to Figma designs through MCP tool calls. Implements structured comment creation with context (node ID, position, content) and supports threaded discussions. Allows agents to flag design issues, suggest improvements, or request clarifications without requiring manual Figma UI interaction.
Unique: Enables programmatic comment creation in Figma through MCP, allowing agents to provide contextual feedback without manual UI interaction. Supports structured comment metadata for categorization and filtering.
vs alternatives: More integrated than external design review tools because feedback stays in Figma context; more scalable than manual review because agents can check designs against rules automatically.
Tracks changes to Figma files over time by querying file version history and computing diffs between versions. Identifies what changed (nodes added/removed/modified), who made changes, and when. Enables LLM agents to understand design evolution and reason about change impact. Implements a change log that can be queried for specific time ranges or node types.
Unique: Exposes Figma's version history through MCP, enabling LLM agents to reason about design changes over time. Implements diff computation to identify specific node modifications rather than just version metadata.
vs alternatives: More accessible than Figma's native version history UI because it's programmatic; enables automated analysis of design change patterns that would be tedious to do manually.
Analyzes Figma designs for responsive design patterns and validates layouts against specified breakpoints. Checks for proper use of constraints, auto-layout, and responsive sizing. Identifies potential responsive design issues (text overflow, layout collapse, unintended scaling). Provides LLM agents with structured feedback on design responsiveness and suggests improvements.
Unique: Analyzes Figma constraint and auto-layout configurations to validate responsive design patterns, providing structured feedback on potential issues. Enables LLM agents to reason about design responsiveness without manual inspection.
vs alternatives: More comprehensive than manual responsive design review because it checks all elements systematically; more actionable than design guidelines because it identifies specific issues and suggests fixes.
+1 more capabilities
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 at 28/100.
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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