Supabase ranks higher at 42/100 vs just-every/mcp-screenshot-website-fast at 26/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | just-every/mcp-screenshot-website-fast | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 26/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Captures full-page website screenshots and automatically tiles them into 1072x1072 pixel chunks (1.15 megapixels) using Sharp image processing, optimizing for Claude Vision API's token efficiency and visual processing constraints. The system constrains all viewport dimensions to maximum 1072x1072 to ensure each tile fits within optimal vision model input boundaries without requiring external image resizing or post-processing.
Unique: Implements automatic tiling specifically calibrated to Claude Vision API's 1.15 megapixel optimal input size, using Sharp for efficient image chunking rather than generic screenshot tools that require manual post-processing. The 1072x1072 constraint is baked into the viewport configuration itself, not applied after capture.
vs alternatives: Unlike Playwright or Puppeteer screenshot methods that capture at arbitrary resolutions requiring external tiling, this tool bakes Claude Vision optimization into the capture pipeline, eliminating post-processing overhead and ensuring consistent token efficiency.
Implements multiple wait strategies (networkIdle, domContentLoaded, custom JavaScript conditions) to ensure dynamic content has fully loaded before capture, with configurable timeouts and retry logic. The system injects JavaScript probes to detect application-specific readiness conditions (e.g., React hydration, data fetch completion) rather than relying solely on browser network events.
Unique: Combines multiple wait strategies (networkIdle, domContentLoaded, custom JavaScript probes) with retry logic and timeout handling, allowing detection of application-specific readiness states via injected JavaScript rather than generic browser events. The architecture supports both framework-agnostic network-based waits and framework-aware custom conditions.
vs alternatives: More sophisticated than Puppeteer's default waitForNavigation (which only handles network events), this system allows custom JavaScript condition injection for framework-specific readiness detection, making it suitable for modern SPAs that don't follow traditional page load patterns.
Uses the Sharp image processing library to efficiently tile full-page screenshots into 1072x1072 chunks, handling image format conversion, compression, and metadata extraction. The tiling pipeline processes captured PNG images through Sharp's streaming API, splitting large images into overlapping or non-overlapping tiles based on configuration, and returning tile metadata with coordinate information.
Unique: Leverages Sharp's high-performance image processing library for efficient tiling, using streaming APIs to minimize memory overhead. The tiling pipeline is optimized for the specific 1072x1072 constraint, avoiding generic image resizing or cropping overhead.
vs alternatives: More efficient than canvas-based tiling or ImageMagick, Sharp provides native Node.js bindings with streaming support, enabling fast tiling of large images without excessive memory consumption or process spawning.
Manages Chromium browser process lifecycle with automatic restart on crash, graceful shutdown on signals (SIGTERM, SIGINT), and connection pooling to reuse browser instances across multiple screenshot operations. The system implements a serve-restart wrapper that monitors the main MCP server process and automatically restarts it if it crashes, maintaining availability for long-running AI agent workflows.
Unique: Implements a two-tier process architecture (serve-restart wrapper + main MCP server) that monitors and auto-restarts the screenshot service on crash, combined with graceful signal handling for clean shutdown. This pattern is distinct from simple browser pooling — it ensures the entire service remains available even if the underlying browser process crashes.
vs alternatives: Unlike Puppeteer or Playwright used directly (which require manual crash handling), this tool wraps the entire screenshot service with automatic restart logic, making it suitable for production AI agent deployments where availability is critical.
Records time-series screenshots of page interactions as WebP animations with adaptive frame rate selection based on content change detection. The system captures PNG frames at configurable intervals, deduplicates identical frames to reduce file size, and encodes the sequence into WebP animations using Sharp, enabling efficient video-like capture of dynamic page behavior without full video codec overhead.
Unique: Combines adaptive frame rate capture with pixel-level deduplication and WebP animation encoding, allowing efficient time-series recording of page state changes. The system injects JavaScript to detect content changes and adjust frame capture intervals dynamically, reducing redundant frames while maintaining visual fidelity.
vs alternatives: More efficient than full video recording (no codec overhead) and more intelligent than fixed-interval frame capture (deduplication reduces file size by 30-50% for static content), making it ideal for AI vision analysis of page interactions without excessive token consumption.
Captures console output (log, error, warn, info) during page execution with full execution context, including message content, severity level, and timestamp. The system injects a JavaScript listener that intercepts console methods and collects messages over a specified duration, returning structured JSON with all captured messages for analysis by AI models.
Unique: Implements JavaScript injection-based console interception that captures all console method calls with structured metadata (level, timestamp, message), providing a machine-readable log of page execution behavior. This is distinct from browser DevTools protocol logging, which requires additional parsing.
vs alternatives: More accessible than raw CDP (Chrome DevTools Protocol) console logging, this approach provides structured JSON output directly suitable for AI analysis without requiring additional parsing or protocol handling.
Exposes screenshot and screencast capabilities as MCP tools via stdio-based JSON-RPC transport, enabling integration with Claude Code, VS Code, Cursor, and JetBrains IDEs. The system implements the Model Context Protocol specification, serializing tool requests/responses as JSON-RPC messages over stdin/stdout, allowing AI assistants to invoke screenshot operations as native tools.
Unique: Implements full Model Context Protocol compliance with stdio JSON-RPC transport, exposing screenshot operations as native MCP tools that Claude and other AI assistants can invoke directly. The architecture includes proper tool schema definition, error handling, and response serialization.
vs alternatives: Unlike REST API or direct library integration, MCP protocol integration allows Claude and other AI assistants to treat screenshot capture as a first-class tool with proper schema validation and error handling, enabling more reliable AI-driven web automation.
Provides a command-line interface (bin/mcp-screenshot-website.js) for direct screenshot capture without MCP server overhead, enabling scripting, testing, and manual screenshot operations. The CLI accepts URL, viewport, wait strategy, and output format parameters, executing the screenshot capture engine directly and returning results as files or base64-encoded output.
Unique: Provides a lightweight CLI entry point that bypasses MCP server overhead for one-off screenshot operations, using the same underlying screenshot engine as the MCP server but with direct process invocation and file-based output.
vs alternatives: Simpler than running a full MCP server for single screenshot operations, this CLI approach is ideal for scripting and testing but trades concurrency and performance for simplicity.
+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 just-every/mcp-screenshot-website-fast at 26/100.
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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