| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes browser control commands directly against Safari's native API layer using AppleScript, bypassing Chromium/WebDriver overhead. Leverages macOS Automation framework to send typed commands (navigate, click, type, scroll) to Safari's runtime without spawning separate processes or maintaining WebDriver connections. Maintains persistent Safari session state including authentication cookies and login sessions across command sequences.
Unique: Uses AppleScript directly against Safari's native Automation framework rather than WebDriver protocol, eliminating Chromium/Selenium overhead and preserving session state without explicit cookie management. Implements 80 discrete automation tools as MCP resources mapped to Safari's native command set.
vs alternatives: Lighter resource footprint and native session persistence vs Selenium/Puppeteer, but locked to macOS and Safari only; faster than remote WebDriver for local automation but less cross-platform flexible.
Exposes 80 Safari automation operations as discrete MCP tools (resources/functions) that AI agents can discover and invoke through the Model Context Protocol. Each tool maps to a specific Safari automation capability (navigate, click, extract text, take screenshot, etc.) with typed parameters, return schemas, and error handling. Implements MCP server interface to advertise tool availability, handle invocation requests, and stream results back to client agents.
Unique: Implements full MCP server specification to expose Safari automation as discoverable, typed tools rather than raw API endpoints. Uses MCP resource/tool abstraction to provide agents with structured capability contracts and automatic parameter validation.
vs alternatives: Cleaner agent integration than custom REST APIs because MCP provides standard discovery and invocation patterns; more flexible than hardcoded agent prompts because tools are dynamically discoverable.
Detects and reports automation errors including navigation failures, element not found errors, timeout conditions, and JavaScript execution errors. Implements page state validation to check for expected elements, error messages, or page conditions before proceeding. Returns structured error objects with error codes, messages, and context information. Supports conditional error handling in agent workflows.
Unique: Provides structured error reporting with context information to enable agent-level error handling and recovery. Implements page state validation as a first-class operation rather than implicit error detection.
vs alternatives: More actionable than generic error messages because it includes context and error codes; better for agent workflows than silent failures because it enables conditional error handling; less comprehensive than dedicated testing frameworks but more integrated with automation.
Executes Safari automation commands without displaying browser windows or UI elements, using AppleScript's background execution mode and Safari's headless-compatible operation flags. Commands run in Safari's background process without visual feedback, allowing agents to perform browser operations without interrupting user workflows or consuming screen real estate. Maintains full automation capability (navigation, interaction, data extraction) while remaining invisible to the user.
Unique: Leverages AppleScript's background execution mode combined with Safari's process isolation to run full browser automation without spawning visible windows. Achieves headless-like behavior on Safari without requiring separate headless browser binary.
vs alternatives: Lighter than Puppeteer/Playwright headless Chrome because it reuses Safari's existing process; more user-friendly than traditional headless browsers because it preserves session state and doesn't require separate binary management.
Maintains Safari's native cookie store, session tokens, and authentication state across automation command sequences by operating against the same Safari process instance. Automation commands execute within the context of the user's existing Safari session, automatically inheriting all stored credentials, cookies, and login state without explicit session management or cookie jar manipulation. Leverages macOS Keychain integration for secure credential storage when needed.
Unique: Avoids explicit session/cookie management by operating within Safari's native process context, automatically inheriting the user's authentication state. Eliminates the need for agents to handle credential passing or cookie jar manipulation.
vs alternatives: More secure than Selenium/Puppeteer approaches that require passing credentials through code; simpler than manual cookie management because it leverages Safari's native session handling; less flexible than explicit session APIs but more user-friendly for authenticated workflows.
Extracts structured content from loaded Safari pages using CSS selectors, XPath expressions, and JavaScript evaluation. Supports querying page DOM to retrieve element text, attributes, computed styles, and nested structures. Returns extracted data as structured JSON objects with optional HTML sanitization. Implements efficient DOM traversal using Safari's native JavaScript engine rather than separate parsing libraries.
Unique: Uses Safari's native JavaScript engine for DOM querying and evaluation rather than separate parsing libraries (BeautifulSoup, jsdom), reducing dependencies and leveraging the browser's native DOM implementation. Supports both declarative selectors and imperative JavaScript for flexible extraction patterns.
vs alternatives: More accurate than regex-based extraction because it uses actual DOM APIs; faster than headless Chromium for simple queries because it reuses Safari's existing process; less flexible than dedicated scraping frameworks but more integrated with browser automation.
Controls Safari's navigation stack by loading URLs, handling redirects, managing browser history (back/forward), and detecting page load completion. Supports both direct URL navigation and relative link following. Implements load-state detection to wait for pages to fully render before returning control to agent, preventing race conditions in multi-step workflows. Handles redirect chains transparently.
Unique: Implements load-state detection using Safari's native page load events rather than polling or timeout-based heuristics, providing more reliable page readiness signals. Handles redirect chains transparently without requiring explicit redirect tracking.
vs alternatives: More reliable than timeout-based approaches because it uses actual page load events; simpler than manual redirect tracking because Safari handles chains automatically; less flexible than low-level WebDriver APIs but more user-friendly for typical workflows.
Simulates user interactions with page elements including mouse clicks, keyboard input, and scrolling operations. Supports clicking by CSS selector or coordinates, typing text into form fields with optional key modulation (shift, ctrl, etc.), and scrolling to specific elements or coordinates. Implements element visibility checking before interaction to prevent clicking hidden elements. Uses AppleScript event simulation for native input handling.
Unique: Uses AppleScript event simulation for native input handling rather than synthetic DOM events, providing more realistic user interaction that triggers native browser handlers. Includes pre-interaction visibility validation to prevent silent failures.
vs alternatives: More reliable than synthetic DOM events because it uses native OS-level input; better error detection than Puppeteer because it validates element visibility before interaction; less flexible than low-level WebDriver but more user-friendly for typical form automation.
+3 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 Safari MCP at 32/100.
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