| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Reads and traverses the complete hierarchical structure of Figma design files via the Figma REST API, exposing the document tree with metadata about pages, frames, components, and layers. Implements recursive tree walking to expose nested layer relationships and properties, enabling programmatic access to design file organization without manual UI navigation.
Unique: Exposes Figma's document tree as a queryable MCP resource, allowing Claude and other LLM clients to directly inspect design file structure without requiring developers to write custom API wrappers or parse raw Figma JSON responses
vs alternatives: Simpler than building custom Figma API clients because it abstracts authentication, pagination, and tree traversal into standard MCP tool calls that work with any MCP-compatible client
Extracts detailed metadata about Figma components including component sets, variants, properties, and documentation. Queries the Figma API to surface component definitions, variant configurations, and property schemas, enabling programmatic discovery of design system components and their configuration options without manual inspection.
Unique: Surfaces Figma component variant schemas and property definitions as structured data, allowing LLM-based agents to reason about component capabilities and generate accurate code bindings without manual schema definition
vs alternatives: More complete than manual component audits because it programmatically discovers all variants and properties, reducing human error and enabling real-time sync with design system changes
Extracts design tokens (colors, typography, spacing, shadows) from Figma files and maps them to semantic names and values. Parses Figma's token plugin format or custom naming conventions to surface design tokens as structured data, enabling synchronization with code-based token systems (CSS variables, design token JSON, etc.).
Unique: Bridges Figma design tokens to code-based token systems by extracting semantic token definitions and mapping them to standard formats (CSS variables, JSON), enabling automated token synchronization without manual copy-paste
vs alternatives: More flexible than Figma Tokens plugin alone because it can extract tokens from custom naming conventions and export to multiple formats, supporting teams with existing token infrastructure
Queries properties of specific frames, layers, and design elements within a Figma file, including dimensions, positioning, fill colors, stroke properties, text content, and constraints. Implements filtered selection logic to retrieve properties for named layers or layers matching criteria, returning structured property objects without requiring manual layer navigation.
Unique: Provides direct property access to Figma layers via MCP tools, allowing LLM agents to query specific design properties without parsing raw API responses or manually navigating the Figma UI
vs alternatives: More efficient than exporting design specs manually because it enables programmatic property queries, supporting automation workflows that would otherwise require human inspection
Registers Figma API operations as MCP tools with JSON schema definitions, enabling Claude and other MCP clients to discover and invoke Figma operations through standardized function calling. Implements schema validation and parameter marshaling to translate MCP tool calls into Figma API requests, with error handling and response transformation.
Unique: Exposes Figma operations as MCP tools with full schema validation, enabling LLM clients to discover and invoke Figma queries through standard function calling without custom API wrappers or authentication handling
vs alternatives: More discoverable than raw Figma API because MCP schema definitions enable LLM clients to understand available operations and parameters, supporting autonomous agent workflows
Handles Figma API authentication via personal access tokens or OAuth, managing token lifecycle and API request signing. Implements token validation and error handling for authentication failures, enabling secure access to Figma resources without exposing credentials in client code.
Unique: Abstracts Figma API authentication into MCP server initialization, allowing clients to invoke Figma tools without managing credentials directly, improving security posture for multi-user deployments
vs alternatives: Simpler than building custom authentication layers because it handles token validation and request signing transparently, reducing security surface area
Supports querying multiple Figma files or resources in a single operation with pagination handling for large result sets. Implements batching logic to reduce API calls and pagination to handle Figma API limits, enabling efficient bulk operations without manual pagination handling.
Unique: Implements transparent pagination and batching for Figma API queries, allowing clients to request large datasets without manual pagination handling or rate limit management
vs alternatives: More efficient than sequential API calls because it batches requests and handles pagination automatically, reducing latency and API call overhead
Normalizes Figma API responses and errors into consistent formats, translating API-specific error codes into human-readable messages. Implements retry logic for transient failures and graceful degradation for partial failures, enabling robust automation workflows that handle API instability.
Unique: Provides automatic retry logic and error normalization for Figma API calls, enabling automation workflows to recover from transient failures without explicit error handling code
vs alternatives: More robust than raw API calls because it implements exponential backoff and error normalization, reducing automation failures due to temporary API issues
+2 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.
Figma MCP Server scores higher at 60/100 vs Supabase at 42/100.
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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