| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Captures full-screen or region-specific screenshots from the host desktop and returns pixel-perfect image data in base64 format, enabling AI agents to visually perceive and analyze the current UI state. Integrates with native OS screenshot APIs (macOS/Linux/Windows) through Node.js bindings, providing sub-100ms capture latency for real-time visual feedback loops in agent decision-making.
Unique: Implements native OS-level screenshot capture through MCP protocol, allowing LLM agents to directly perceive desktop state without requiring separate screenshot tools or browser automation libraries; uses base64 encoding for seamless integration with vision-capable LLMs
vs alternatives: Provides lower latency and higher fidelity desktop perception than browser-only solutions like Playwright, and integrates natively into MCP agent workflows without requiring separate tool orchestration
Enables precise mouse cursor positioning and click operations (single-click, double-click, right-click) at specified screen coordinates, translating high-level agent intents into low-level input events. Uses native OS input APIs (Xdotool on Linux, CGEvent on macOS, SendInput on Windows) to simulate human-like mouse interactions with configurable timing and movement curves to avoid detection as automated input.
Unique: Abstracts OS-specific input APIs (Xdotool, CGEvent, SendInput) behind a unified MCP interface, allowing agents to perform mouse interactions without knowledge of underlying platform; includes configurable movement curves and timing to simulate human-like interaction patterns
vs alternatives: Provides cross-platform mouse automation in a single MCP tool without requiring separate platform-specific libraries, and integrates directly into agent decision loops unlike standalone automation frameworks
Maintains a detailed audit trail of all operations performed by agents, including operation type, parameters, timestamp, and result. Logs are stored locally and can be retrieved through MCP interface for debugging, compliance, or workflow analysis. Implements structured logging with configurable verbosity levels and optional sensitive data redaction for security-sensitive operations.
Unique: Provides structured operation logging with configurable verbosity and sensitive data redaction, maintaining an audit trail of all agent operations for compliance and debugging
vs alternatives: Integrates audit logging directly into MCP server with sensitive data redaction, whereas most automation frameworks require external logging infrastructure
Simulates keyboard input including text typing, individual key presses, and multi-key hotkey combinations (Ctrl+C, Cmd+Z, etc.) at the OS level. Implements key event queuing with configurable inter-key delays to simulate human typing speed, and supports modifier key combinations for application shortcuts. Routes through native OS keyboard APIs to ensure compatibility with applications that validate input source.
Unique: Provides unified keyboard input abstraction across Windows/macOS/Linux with support for both text typing and hotkey combinations, including configurable inter-key delays to simulate human typing patterns and avoid input detection systems
vs alternatives: Combines text input and hotkey simulation in a single MCP tool with human-like timing, whereas most automation frameworks require separate libraries for keyboard vs hotkey handling
Implements a complete MCP (Model Context Protocol) server that exposes computer-use capabilities as standardized MCP resources and tools, enabling any MCP-compatible client (Claude, custom agents, etc.) to discover and invoke desktop automation functions. Uses JSON-RPC 2.0 transport over stdio or network sockets, with automatic capability advertisement through MCP's resource and tool schemas.
Unique: Implements a full MCP server that standardizes computer-use capabilities as discoverable MCP tools and resources, allowing any MCP-compatible client to access desktop automation without custom integration code; uses JSON-RPC 2.0 for reliable request/response handling
vs alternatives: Provides a standards-based integration point for desktop automation that works with any MCP client (Claude, custom agents, etc.), whereas point-to-point integrations require reimplementation for each client
Detects and handles multiple physical monitors and virtual display configurations, allowing agents to capture screenshots and perform interactions across the entire display landscape. Maintains a coordinate system that maps logical screen positions to physical monitor positions, enabling agents to work with multi-monitor setups without explicit monitor selection. Automatically detects display topology changes and updates coordinate mappings.
Unique: Automatically detects and maps multi-monitor topologies, allowing agents to work with global screen coordinates without explicit monitor selection; maintains coordinate system consistency across display topology changes
vs alternatives: Provides transparent multi-monitor support without requiring agents to understand display topology, whereas most automation tools require explicit monitor selection or coordinate offset calculation
Enumerates open application windows on the desktop and provides window focus control, allowing agents to switch between applications and ensure keyboard/mouse input targets the correct window. Returns window metadata including title, process ID, window bounds, and focus state. Implements platform-specific window management (wmctrl on Linux, NSWindow API on macOS, Windows API on Windows) with a unified interface.
Unique: Provides unified window enumeration and focus control across Windows/macOS/Linux, abstracting platform-specific window manager APIs (wmctrl, NSWindow, Windows API) behind a single interface
vs alternatives: Combines window enumeration and focus control in a single MCP tool, whereas most automation frameworks require separate window management libraries or platform-specific code
Provides read and write access to the system clipboard, enabling agents to exchange text data with applications through copy/paste operations. Implements platform-specific clipboard APIs (xclip on Linux, NSPasteboard on macOS, Windows Clipboard API) with support for both text and rich text formats. Allows agents to retrieve clipboard contents for verification or use clipboard as a data exchange mechanism.
Unique: Provides unified clipboard read/write access across Windows/macOS/Linux, abstracting platform-specific clipboard APIs and enabling clipboard-based data exchange in agent workflows
vs alternatives: Integrates clipboard operations directly into MCP tool interface, allowing agents to use copy/paste as a data exchange mechanism without requiring separate clipboard management libraries
+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 @github/computer-use-mcp at 38/100.
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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