| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Captures the complete hierarchical structure of Windows UI elements using native UI Automation COM APIs, building an accessibility tree that maps all interactive controls, their properties, and spatial relationships without requiring computer vision. The Tree Service maintains a cached, queryable representation of the desktop state that enables LLMs to understand the current UI layout and identify targets for automation actions.
Unique: Uses Windows native UI Automation COM APIs instead of computer vision or pixel-based detection, providing reliable element identification across all Windows applications without ML model dependencies. Implements dual-mode capture: standard UI tree for desktop apps and filtered DOM mode for browsers that strips browser UI chrome.
vs alternatives: More reliable than vision-based automation (PyAutoGUI, Selenium screenshot analysis) because it accesses the actual UI element hierarchy rather than inferring from pixels, and works with any LLM without requiring vision capabilities.
Simulates user input across multiple modalities (mouse clicks, keyboard typing, scrolling, mouse movement, keyboard shortcuts) by translating MCP tool calls into Windows input events through the UI Automation framework. Each action type is optimized for its use case: click operations target specific UI elements by coordinate or element reference, type operations handle text input with clipboard fallback for large payloads, and scroll/move operations support both absolute and relative positioning.
Unique: Implements multi-modal input through UI Automation APIs with intelligent fallbacks: uses clipboard for large text payloads to avoid character-by-character typing delays, supports both element-based and coordinate-based targeting, and handles keyboard shortcuts through native Windows input event generation.
vs alternatives: More reliable than pyautogui or keyboard libraries because it integrates with Windows UI Automation framework for element-aware targeting, and faster than character-by-character typing for large text blocks through clipboard optimization.
Uses FastMCP's async lifespan context manager to coordinate initialization and cleanup of core services (Desktop Service, Tree Service, WatchDog Service) across the MCP server lifecycle. Services are initialized on server startup and properly cleaned up on shutdown, ensuring resource management and state consistency. The lifespan pattern enables dependency injection and ordered initialization of services.
Unique: Implements service lifecycle management through FastMCP's async lifespan context manager, enabling coordinated initialization and cleanup of multiple services with dependency ordering and proper resource management.
vs alternatives: More robust than manual service initialization because it uses context managers for guaranteed cleanup, and more maintainable than scattered initialization code because services are initialized in a single, ordered location.
Supports configuration through environment variables for transport mode (local/remote), server endpoints, logging levels, and feature flags. Configuration is read at startup and applied across all services, enabling deployment flexibility without code changes. The manifest.json file defines server metadata and tool availability, allowing clients to discover capabilities.
Unique: Implements configuration through environment variables with manifest.json metadata discovery, enabling deployment flexibility and client-side capability discovery without code changes.
vs alternatives: More flexible than hardcoded configuration because it supports environment-based customization, and more discoverable than undocumented configuration because manifest.json provides client-side capability discovery.
Designed with minimal external dependencies, relying primarily on Python standard library and FastMCP framework. Windows UI Automation is accessed through native COM interfaces rather than heavy third-party libraries. This minimizes installation size, reduces dependency conflicts, and improves deployment reliability. The project uses UV (Astral) for dependency management, providing fast, deterministic package resolution.
Unique: Minimizes external dependencies by leveraging Python standard library and native Windows COM interfaces, using UV for fast dependency resolution and enabling lightweight deployment without heavy third-party libraries.
vs alternatives: Lighter weight than automation frameworks with heavy dependencies (Selenium, Playwright), and faster to install and deploy due to minimal external requirements.
Published under MIT license with full source code available on GitHub, enabling community contributions, customization, and transparency. The project includes contribution guidelines, development setup documentation, and code quality standards. Open-source licensing allows integration into commercial products and custom deployments without licensing restrictions.
Unique: Published under permissive MIT license with full source code transparency, enabling community contributions and commercial integration without licensing restrictions.
vs alternatives: More flexible than proprietary automation tools because it allows customization and commercial use, and more transparent than closed-source solutions because full source code is available for audit and modification.
Manages Windows application launching, window control, and process termination through native Windows APIs integrated into the MCP tool layer. Enables starting applications by path or name, bringing windows to focus, minimizing/maximizing/closing windows, and terminating processes. The Desktop Service coordinates these operations with the UI Automation layer to maintain consistent state tracking.
Unique: Integrates process control with the UI Automation state tracking system, ensuring that launched applications are immediately discoverable in the UI element tree and window state is synchronized across the MCP tool layer.
vs alternatives: More integrated than standalone process management libraries because it coordinates with the UI Automation layer for state consistency, and provides window-level control (focus, minimize, maximize) in addition to process-level operations.
Implements a specialized 'DOM mode' for browser automation that extracts the actual web page content structure while intelligently filtering out browser UI elements (address bar, tabs, toolbars, scrollbars). This is achieved by parsing the browser's accessibility tree and applying heuristics to distinguish page content from browser chrome, returning a clean DOM representation that LLMs can reason about without visual noise.
Unique: Applies intelligent filtering to the browser's accessibility tree to separate page content from browser UI chrome, providing a clean DOM representation without requiring computer vision or page screenshot analysis.
vs alternatives: Cleaner than Selenium's raw DOM extraction because it filters browser UI elements, and more reliable than vision-based web automation because it works with the actual DOM structure rather than pixel analysis.
+6 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 Windows-MCP at 34/100.
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