| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability utilizes a structured parsing approach to extract content from any webpage and convert it into clean, LLM-ready Markdown format. It employs a combination of DOM traversal and semantic analysis to identify and retain the most relevant content while removing extraneous elements like ads or navigation bars. This ensures that the output is not only clean but also contextually rich, making it ideal for LLM consumption.
Unique: Utilizes a hybrid approach of semantic analysis and DOM parsing to ensure high-quality content extraction, unlike simpler regex-based solutions.
vs alternatives: More accurate and context-aware than basic scrapers that rely solely on regex, leading to better LLM readiness.
This capability allows the server to handle dynamic content by simulating user interactions or waiting for JavaScript to execute before extracting the final rendered HTML. It leverages headless browser technology to ensure that all content is fully loaded and accessible, which is crucial for modern web applications that rely heavily on client-side rendering.
Unique: Incorporates headless browser technology for dynamic content extraction, setting it apart from traditional scrapers that only process static HTML.
vs alternatives: More reliable than basic scrapers for dynamic sites, ensuring all content is captured accurately.
This capability allows users to define custom extraction rules using a simple configuration format. Users can specify which HTML elements to include or exclude, enabling tailored content extraction based on their specific needs. This is achieved through a flexible rule engine that interprets user-defined criteria and applies them during the extraction process.
Unique: Features a user-friendly rule engine that allows for highly customizable extraction processes, unlike rigid scraping tools.
vs alternatives: Offers greater flexibility than standard scrapers, allowing for tailored content extraction based on user needs.
This capability enables the server to process multiple URLs in a single request, extracting and converting content from each into Markdown format. It uses asynchronous processing to handle multiple requests simultaneously, optimizing throughput and reducing overall extraction time. This is particularly useful for users needing to scrape large volumes of content quickly.
Unique: Utilizes asynchronous processing to handle batch requests efficiently, unlike many tools that process URLs sequentially.
vs alternatives: Significantly faster than sequential processing methods, allowing for rapid content aggregation.
This capability provides robust error handling and logging mechanisms to track issues during the extraction process. It captures errors related to network requests, parsing failures, and rule violations, providing detailed logs that help users diagnose and resolve issues quickly. This is implemented through a centralized logging system that records events and errors in real-time.
Unique: Features a centralized logging system that provides real-time insights into the extraction process, enhancing debugging capabilities.
vs alternatives: More comprehensive than basic logging solutions, allowing for proactive issue resolution.
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 Skrape MCP Server at 24/100. Skrape MCP Server leads on ecosystem, while Supabase is stronger on quality.
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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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