| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Records user interactions (clicks, typing, navigation) in a live browser session by instrumenting Playwright's event listeners and capturing DOM snapshots at each interaction point. Stores interaction sequences with full DOM state, element selectors, and coordinate data to enable deterministic replay and agent learning from human demonstrations.
Unique: Captures full DOM state alongside interaction metadata at each step, enabling agents to understand both the action taken and the resulting page state — most record-replay tools only store action sequences without semantic context
vs alternatives: Provides richer training signal than simple action logs because agents can learn from DOM deltas and element state changes, not just coordinate-based clicks
Replays recorded interaction sequences by resolving stored selectors (CSS, XPath, or coordinate-based) against the current DOM and executing the corresponding Playwright actions (click, type, navigate). Handles selector drift by falling back to alternative selector strategies and validates element visibility/interactability before execution.
Unique: Implements multi-strategy selector resolution (CSS → XPath → coordinate fallback) with visibility validation, allowing replay to adapt to minor DOM changes rather than failing on first selector miss
vs alternatives: More robust than coordinate-only replay (used by RPA tools) because it uses semantic selectors that survive layout changes, but more flexible than strict CSS matching by supporting fallback strategies
Provides built-in assertions for validating interaction outcomes: element visibility, text content matching, URL changes, network request completion. Supports both immediate assertions (after each interaction) and deferred assertions (after workflow completion), enabling agents to verify that interactions succeeded and pages reached expected states.
Unique: Integrates assertions directly into interaction execution flow, allowing agents to validate outcomes inline rather than as separate test steps — enables reactive error handling based on assertion failures
vs alternatives: More integrated than external test frameworks (like pytest) because assertions are part of the automation runtime, enabling real-time error recovery rather than post-execution failure reporting
Exposes recording and replay capabilities as MCP (Model Context Protocol) tools that LLM agents can invoke through a standardized interface. Implements MCP server protocol with tool definitions for start-recording, stop-recording, and replay-interaction, allowing Claude, other LLMs, and agent frameworks to orchestrate browser automation without direct library imports.
Unique: Implements full MCP server protocol for browser automation, allowing stateless tool invocations from LLMs rather than requiring agents to manage browser session state directly — treats recording/replay as composable LLM-callable tools
vs alternatives: Enables LLM agents to use web automation without custom integration code, unlike browser-use libraries that require agent framework-specific adapters
Selects elements for interaction using a cascading strategy: first attempts CSS selectors, falls back to XPath expressions, then uses coordinate-based selection as last resort. Validates element interactability (visibility, clickability) before returning and caches selector strategies that work for future reference, enabling robust element targeting across dynamic UIs.
Unique: Implements intelligent fallback chain with selector strategy caching — learns which selector type works for each element and reuses it, reducing retry overhead on subsequent interactions
vs alternatives: More resilient than single-strategy selectors (pure CSS or XPath) because it adapts to DOM changes, but more performant than brute-force fuzzy matching because it caches successful strategies
Chains multiple recorded or programmatic interactions into a single executable workflow by composing interaction objects with dependency tracking and state validation between steps. Supports conditional branching based on page state (e.g., 'if element exists, click it; otherwise navigate') and error recovery strategies (retry with backoff, alternative action path).
Unique: Supports declarative workflow composition with state-based branching, allowing agents to define conditional paths without imperative control flow — workflows are data structures that can be generated by LLMs
vs alternatives: More flexible than simple replay (which is linear) because it supports branching, but simpler than full workflow engines (like Zapier) because it's specialized for browser interactions
Captures full DOM snapshots at interaction points and computes diffs between consecutive states to identify what changed (new elements, removed elements, attribute changes, text content changes). Provides structured representation of page state changes that agents can reason about, enabling learning from state transitions rather than just action sequences.
Unique: Computes semantic diffs of DOM state (not just raw HTML diffs) by tracking element identity, attribute changes, and content mutations — enables agents to reason about 'what changed' at a semantic level
vs alternatives: Richer than simple screenshot comparison (which is pixel-based and fragile) because it provides structured DOM-level changes that agents can reason about programmatically
Manages Playwright browser instances, pages, and contexts with automatic lifecycle handling (launch, create page, close on error). Supports context isolation for parallel recording sessions and provides utilities for managing browser state (cookies, local storage, authentication) across interactions, enabling reproducible automation with consistent browser environment.
Unique: Provides context-aware session management that isolates recording sessions and preserves browser state, treating each recording as an independent experiment with its own browser context
vs alternatives: More robust than manual Playwright usage because it handles cleanup and error cases automatically, and more flexible than headless browser services because it runs locally with full control
+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 web-agent-protocol at 31/100.
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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