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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Converts plain English task descriptions into executable browser actions by sending simplified DOM state and user instructions to OpenAI's GPT models, which determine the next action (click, form fill, navigation) in a multi-step action cycle. The extension maintains a 50-action limit per task and uses the LLM's reasoning to map user intent to specific DOM elements and interactions.
Unique: Uses a stateful action cycle with DOM simplification to reduce token overhead, sending only interactive elements to the LLM rather than full page HTML. The background service worker orchestrates multi-step reasoning where the LLM observes results after each action before determining the next step, enabling adaptive task completion.
vs alternatives: More accessible than Selenium/Playwright for non-technical users because it interprets English instructions directly rather than requiring code, but slower and more expensive than traditional automation frameworks due to per-action LLM inference.
The content script extracts the full webpage DOM and applies simplification heuristics to reduce token count before sending to the LLM, focusing on interactive elements (buttons, inputs, links) while removing styling, scripts, and non-interactive content. This preprocessing step runs in the page context and communicates results back to the background service worker via Chrome's message passing API.
Unique: Implements a two-stage extraction pipeline: content script runs in page context for direct DOM access, then sends simplified structure to background worker via Chrome message passing. This avoids serialization overhead and enables real-time element interaction without re-querying the DOM.
vs alternatives: More efficient than sending full HTML to LLMs because it pre-filters to interactive elements, reducing token usage by 60-80% compared to raw DOM, but less precise than tree-sitter-based AST parsing used in code-aware tools.
The LLM determines when a task is complete by analyzing the current DOM state and action history, returning a 'complete' action type when the goal is achieved. The background service worker monitors for completion signals, task timeout (50-action limit), or explicit user termination via the popup UI. Upon completion, the extension displays a summary of executed actions and results to the user.
Unique: Implements a dual-mode termination strategy: LLM-driven completion detection for autonomous workflows and user-initiated termination via the popup UI for manual control. The 50-action limit provides a safety mechanism to prevent runaway tasks.
vs alternatives: More user-friendly than silent task execution because it provides explicit completion signals and allows manual termination, but less sophisticated than workflow engines with conditional logic and error handling.
The extension uses Webpack to bundle TypeScript source code, React components, and dependencies into separate bundles for the background worker, content script, popup, and DevTools panel. The build process generates a manifest.json file with correct entry points, applies code splitting to optimize bundle sizes, and outputs a packaged extension ready for Chrome installation. Development mode includes hot reloading for faster iteration.
Unique: Uses Webpack to generate separate bundles for each extension context (background worker, content script, popup, DevTools), with shared code extracted into common chunks. This approach optimizes bundle sizes while maintaining clear separation of concerns.
vs alternatives: More flexible than pre-built extension templates because it allows custom configuration and dependency management, but more complex to set up than simpler build tools like esbuild or Parcel.
Executes browser actions (clicks, form fills, navigation) using Chrome's debugger API rather than standard DOM events, providing more reliable interaction with modern web applications that use event delegation or custom event handlers. The content script receives action instructions from the background worker and translates them into debugger protocol commands for precise element targeting and interaction.
Unique: Uses Chrome's native debugger protocol for element interaction instead of injected JavaScript, bypassing event handler interception and providing direct control over user input simulation. This approach is more robust for modern SPAs but adds latency compared to DOM-based alternatives.
vs alternatives: More reliable than Puppeteer/Playwright for sites with aggressive event handling because it uses the browser's native protocol rather than JavaScript injection, but slower due to debugger overhead and less flexible than headless browser APIs for complex scenarios.
Maintains a stateful action history throughout task execution, allowing the LLM to observe results after each action before determining the next step. The background service worker stores action history in memory (via Zustand state management) and includes it in subsequent LLM prompts, enabling the model to adapt based on actual page state changes and detect task completion or failure conditions.
Unique: Implements a closed-loop action cycle where the LLM receives the full action history and current DOM state before each decision, enabling adaptive behavior without external state stores. Zustand manages state in the background worker, providing reactive updates to the UI without manual synchronization.
vs alternatives: More transparent than black-box automation tools because action history is visible to users and developers, but less scalable than distributed workflow engines because state is in-memory and limited to 50 actions.
Provides a React-based popup interface (built with Chakra UI) where users enter natural language task descriptions and view real-time execution results. The popup communicates with the background service worker via Chrome's message passing API, displaying action history, current DOM state, and task completion status. State is managed via Zustand, enabling reactive UI updates as the automation progresses.
Unique: Uses Chakra UI for accessible, responsive component design within the Chrome popup constraint, with Zustand for state synchronization between popup and background worker. This enables real-time UI updates without manual polling or complex message handling.
vs alternatives: More user-friendly than command-line or code-based automation tools because it provides a visual interface for task input and result viewing, but less powerful than full IDE-based tools for complex workflow definition.
Provides an alternative interface in Chrome DevTools (separate from the popup) for advanced users to inspect DOM state, view LLM prompts and responses, and debug action execution. The DevTools panel has access to the same background worker state via Zustand and can display detailed information about each action cycle, including the simplified DOM sent to the LLM and the model's reasoning.
Unique: Integrates with Chrome DevTools API to provide a dedicated debugging interface alongside the popup, giving developers visibility into the full action cycle including LLM prompts, responses, and DOM state without modifying extension code.
vs alternatives: More integrated than external logging tools because it leverages Chrome's native DevTools infrastructure, but less flexible than custom logging because it's limited to the DevTools panel UI.
+4 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 Taxy AI at 23/100.
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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