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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Captures visual snapshots of active macOS windows and returns them as image data that AI agents can process. Implements native macOS APIs (likely CGWindowListCreateImage or similar) to grab window content at the pixel level, enabling agents to understand UI state, form layouts, and visual information without parsing HTML or DOM structures. Privacy-first design keeps all image data local to the machine.
Unique: Implements MCP protocol for screenshot delivery, allowing AI agents to request visual context on-demand through a standardized tool interface rather than polling or event-driven approaches. Privacy-first architecture ensures images never leave the local machine.
vs alternatives: Unlike cloud-based screenshot services (e.g., Anthropic's vision API with external screenshots), Screeny keeps all visual data local and integrates directly into MCP agent workflows without requiring external APIs or image uploads.
Exposes screenshot capture as an MCP tool that AI agents can invoke through standard function-calling interfaces. Implements the MCP server protocol to register a callable tool with schema validation, allowing agents to request screenshots with optional parameters (window ID, region bounds, format). Handles tool invocation routing and response serialization back to the agent.
Unique: Implements MCP server protocol natively, allowing screenshot requests to be treated as first-class tools in agent workflows rather than external API calls. Supports schema-based parameter validation for window selection and capture options.
vs alternatives: More integrated than REST API approaches because it uses MCP's native tool protocol, reducing latency and allowing agents to compose screenshot requests with other tools in a single reasoning step.
Ensures all screenshot data remains on the local machine without transmission to external servers or cloud APIs. Implements a local-only architecture where image capture, storage, and delivery happen entirely within the MCP server process. No telemetry, no image logging to external services, and no intermediate cloud processing steps.
Unique: Implements a zero-transmission architecture where screenshots are generated and consumed entirely within the local MCP server process, with no intermediate cloud hops or external API calls. Contrasts with vision API approaches that require image uploads.
vs alternatives: Provides stronger privacy guarantees than cloud-based vision APIs (e.g., Claude Vision, GPT-4V) because images never leave the local machine, making it suitable for handling sensitive UI content without compliance concerns.
Allows agents to request screenshots of specific windows by window identifier or title matching, rather than capturing the entire screen. Implements window enumeration and filtering logic to locate target windows and capture only their content. Supports optional region-of-interest cropping to capture specific UI elements within a window.
Unique: Implements window enumeration and filtering to allow agents to target specific windows by ID or title, reducing image payload size and enabling focused automation on multi-window systems. Supports optional ROI cropping for further optimization.
vs alternatives: More efficient than full-screen capture because it reduces image size and processing overhead, allowing agents to focus on relevant UI areas and reducing latency in multi-window environments.
Enables agents to capture screenshots before and after taking actions (e.g., clicking buttons, typing text), creating a visual feedback loop for verification and error detection. Agents can request screenshots, take an action via another tool, then request another screenshot to verify the action succeeded. Supports sequential screenshot requests within a single agent reasoning step.
Unique: Integrates screenshot capability into agent reasoning loops, allowing agents to use visual feedback as part of their decision-making process. Enables agents to verify actions and detect failures without relying on application-specific APIs or event listeners.
vs alternatives: More robust than API-based automation because it detects visual state changes regardless of application type, making it suitable for automating legacy UIs, web apps, and custom applications without requiring application-specific integrations.
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 Screeny 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
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