| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to target web elements using natural language commands, leveraging NLP techniques to parse and understand user input. It employs a context-aware parsing engine that translates user instructions into specific DOM manipulations, enabling it to handle dynamic pages and complex user flows effectively. This approach distinguishes it from traditional selectors by making it more intuitive for non-technical users.
Unique: Utilizes an advanced NLP engine to interpret natural language commands, making web automation accessible to users without coding skills.
vs alternatives: More user-friendly than Selenium for non-developers due to its natural language interface.
This capability automates interactions with dynamic web pages by monitoring changes in the DOM and responding to events in real-time. It uses a reactive programming model to listen for changes and execute user-defined actions, ensuring that the automation adapts to the evolving state of the web application. This allows for seamless interaction with AJAX-loaded content and other dynamic elements.
Unique: Incorporates a reactive programming model to handle real-time changes in web applications, allowing for robust automation of dynamic content.
vs alternatives: More effective than traditional tools for single-page applications due to its real-time monitoring capabilities.
This capability generates detailed reports based on the outcomes of web automation tasks, compiling logs and results into structured formats like CSV or JSON. It uses a templating system to allow users to customize report formats and content, ensuring that the generated reports meet specific needs. This feature is particularly useful for QA teams and data analysts who need to document web interactions.
Unique: Features a customizable templating system for report generation, allowing users to tailor outputs to their specific reporting needs.
vs alternatives: More flexible than built-in reporting tools in other automation frameworks due to its customizable templates.
This capability enables users to define and execute multi-step workflows that involve various web interactions, such as clicking, typing, and data extraction, in a sequential manner. It employs a state machine architecture to manage the flow of actions, ensuring that each step is completed before proceeding to the next. This structured approach allows for complex automation scenarios to be executed reliably.
Unique: Utilizes a state machine architecture to manage complex workflows, ensuring reliable execution of multi-step processes.
vs alternatives: More reliable than simple scripting solutions due to its structured state management.
This capability allows users to extract structured data from specified web elements, using a combination of CSS selectors and XPath queries. It provides a user-friendly interface for defining extraction rules and supports output in various formats, including JSON and CSV. This makes it easy to gather data from complex web pages without needing to write extensive code.
Unique: Combines CSS selectors and XPath queries in a user-friendly interface, making data extraction accessible without extensive coding.
vs alternatives: Easier to use than traditional scraping libraries due to its intuitive interface.
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 shaft-mcp at 29/100.
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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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