| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Captures complete webpage screenshots including content below the fold by rendering the full DOM and scrolling through the entire page height. Uses headless browser automation (likely Puppeteer or Playwright) to load pages, wait for dynamic content, and serialize the full rendered output as PNG/JPEG, handling variable page heights and responsive layouts automatically.
Unique: Implements full-page capture through MCP protocol integration, allowing Claude and other LLM clients to request screenshots as a native tool without custom HTTP endpoints or external services
vs alternatives: Provides full-page screenshots via MCP's standardized tool interface, eliminating the need for separate screenshot APIs or custom webhook infrastructure compared to standalone screenshot services
Captures screenshots of specific DOM elements identified by CSS selectors or XPath expressions. The tool renders the page, locates the target element, measures its bounding box, and extracts only that region from the rendered output, enabling focused visual inspection without capturing surrounding page content.
Unique: Provides selector-based element extraction through MCP, allowing LLM agents to request specific component screenshots by CSS selector without parsing page HTML or managing browser state directly
vs alternatives: More precise than full-page screenshots for component testing and reduces image size/processing overhead by capturing only the target element region
Captures screenshots at predefined device viewport sizes (mobile, tablet, desktop) by configuring the headless browser's viewport dimensions before rendering. Applies device-specific user agents and viewport metrics to simulate how pages render across different screen sizes, enabling responsive design validation without manual device testing.
Unique: Integrates device profile management with MCP tool interface, allowing agents to request screenshots at specific device sizes without managing viewport configuration or user agent strings
vs alternatives: Enables responsive testing through a single MCP tool call rather than requiring separate API calls per device or manual browser resizing
Registers screenshot capture functions as standardized MCP tools with JSON schema definitions that describe input parameters, output types, and tool behavior. The schema enables Claude and other MCP clients to understand available screenshot operations, validate inputs, and parse responses without custom integration code.
Unique: Implements screenshot operations as first-class MCP tools with full schema support, enabling Claude to discover and invoke screenshot capabilities through the standard MCP protocol without custom adapters
vs alternatives: Provides native MCP integration compared to screenshot APIs that require custom HTTP clients or wrapper code to integrate with LLM agents
Processes screenshot requests asynchronously through the MCP message queue, allowing multiple concurrent screenshot operations without blocking the main event loop. Uses Promise-based browser automation and async/await patterns to manage headless browser lifecycle, page navigation, and image rendering in parallel.
Unique: Leverages async/await patterns with MCP's message-based architecture to handle concurrent screenshot requests without blocking the LLM client, enabling responsive agent behavior
vs alternatives: Provides non-blocking screenshot capture compared to synchronous screenshot APIs that would stall agent execution during rendering
Implements intelligent waiting mechanisms that detect when dynamically-loaded content has finished rendering before capturing screenshots. Uses strategies like waiting for network idle, monitoring DOM mutations, polling for specific elements, or explicit wait conditions to ensure JavaScript-heavy pages are fully rendered before image capture.
Unique: Provides configurable wait strategies through MCP tool parameters, allowing agents to specify how to detect render completion without hardcoding page-specific logic
vs alternatives: Handles dynamic content better than simple screenshot tools by offering multiple wait strategies (network idle, DOM mutations, element polling) rather than fixed delays
Allows configuration of output image format (PNG, JPEG), compression quality, and rendering options through tool parameters. Enables callers to optimize for file size vs. visual fidelity based on use case, supporting both lossless PNG for precise visual comparison and lossy JPEG for bandwidth-efficient transmission.
Unique: Exposes format and quality configuration through MCP tool parameters, allowing agents to optimize image output based on downstream requirements without managing encoding separately
vs alternatives: Provides format flexibility within a single tool compared to screenshot services that offer only fixed output formats
Implements comprehensive error handling for screenshot failures including network errors, timeout conditions, rendering failures, and invalid inputs. Returns structured error responses with diagnostic information (error type, timeout details, page load status) that help agents understand why a screenshot failed and potentially retry with different parameters.
Unique: Provides structured error responses through MCP that include diagnostic context (page load status, timeout details, browser errors), enabling agents to make informed retry decisions
vs alternatives: Returns detailed error information compared to screenshot APIs that only indicate success/failure without diagnostic context
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 ScreenshotMCP at 21/100.
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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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