Supabase ranks higher at 42/100 vs BrowserOS – "Claude Cowork" in the browser at 38/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | BrowserOS – "Claude Cowork" in the browser | Supabase |
|---|---|---|
| Type | Framework | MCP Server |
| UnfragileRank | 38/100 | 42/100 |
| Adoption | 1 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Establishes direct WebSocket or HTTP connections to Anthropic's Claude API from the browser runtime, implementing streaming message handlers that progressively render token-by-token responses without server-side proxying. Uses fetch-based request construction with automatic retry logic and connection pooling to maintain low-latency interactions with Claude models (3.5 Sonnet, Opus, Haiku).
Unique: Implements direct browser-to-Claude API communication without intermediate server, using streaming WebSocket handlers that render responses token-by-token with minimal latency, differentiating from typical SaaS architectures that proxy through backend servers
vs alternatives: Eliminates server infrastructure costs and latency compared to traditional Claude integrations, though trades security (exposed API keys) for simplicity
Provides a shared browser-based workspace where multiple users can simultaneously interact with Claude, with real-time state synchronization across clients using operational transformation or CRDT-based conflict resolution. Implements presence awareness (cursor positions, active selections) and message history merging to maintain consistency across concurrent edits and Claude interactions.
Unique: Implements collaborative workspace with real-time synchronization directly in browser using operational transformation or CRDT patterns, enabling multi-user Claude interactions without requiring a centralized collaboration server, unlike tools like Figma that depend on backend state management
vs alternatives: Provides real-time collaboration features comparable to Google Docs but for Claude-powered workflows, with lower infrastructure overhead than traditional SaaS collaboration platforms
Implements an agentic loop where Claude can call tools (functions) defined in the browser, with automatic tool result handling and replanning. Supports function schema definition (JSON Schema) and automatic tool invocation based on Claude's function_calls output. Implements multi-turn agent loops where Claude reasons about tool results and decides next steps, with execution logging and error recovery for failed tool calls.
Unique: Implements a browser-native agent framework with tool calling and multi-turn planning loops, enabling autonomous Claude agents to execute tasks using browser-based tools without external agent infrastructure, differentiating from cloud-based agent platforms
vs alternatives: Provides low-latency autonomous agents with instant tool execution compared to cloud-based agent services, though limited to browser-executable tools
Indexes the user's local codebase (JavaScript, TypeScript, Python, etc.) into an in-browser vector store or AST-based symbol table, then uses Claude to generate code completions that reference existing functions, classes, and patterns from the indexed codebase. Implements semantic search across indexed symbols to retrieve relevant context before sending completion requests to Claude, enabling structurally-aware suggestions that respect project conventions.
Unique: Implements local codebase indexing and semantic search entirely in the browser using WASM-based AST parsing and in-memory vector stores, enabling context-aware Claude completions without sending code to external servers, unlike GitHub Copilot which relies on cloud-based indexing
vs alternatives: Provides privacy-preserving codebase-aware completions compared to cloud-based alternatives, with faster latency for small-to-medium projects due to local indexing
Enables users to construct Claude prompts by attaching files (images, PDFs, code snippets, documents) directly in the browser, with automatic format detection and encoding. Implements file preview rendering (image thumbnails, code syntax highlighting, PDF text extraction) and drag-and-drop file upload with progress tracking. Converts files to base64 or Claude-compatible formats (vision tokens for images, text extraction for documents) before sending to Claude API.
Unique: Implements client-side file handling with preview rendering and format conversion entirely in the browser, avoiding server-side file storage and enabling immediate visual feedback on attachments before Claude processing, unlike web-based Claude interfaces that require server-side file handling
vs alternatives: Provides privacy-preserving file attachment handling with instant local previews, reducing latency and infrastructure costs compared to server-based file upload systems
Persists Claude conversation history to browser local storage (IndexedDB or localStorage) with automatic serialization of messages, metadata, and context. Implements conversation search across stored history using full-text indexing, and provides export functionality (JSON, Markdown, PDF) to save conversations locally. Handles storage quota management (typically 5-50MB per domain) with automatic cleanup of old conversations or user-initiated archival.
Unique: Implements persistent conversation storage entirely in browser using IndexedDB with full-text search and multi-format export, enabling offline access to conversation history without requiring backend database or cloud sync infrastructure
vs alternatives: Provides instant conversation persistence and search without server infrastructure, though trades cloud backup and cross-device sync for privacy and simplicity
Provides a templating engine for constructing reusable Claude prompts with variable placeholders (e.g., {{variable_name}}, {{file_content}}, {{timestamp}}). Implements a prompt library UI where users can save, organize, and version-control prompt templates. Supports conditional blocks (if/else) and loops for dynamic prompt generation based on user inputs or file content, with real-time preview of rendered prompts before sending to Claude.
Unique: Implements a browser-native prompt templating system with visual editor and library management, enabling non-technical users to create and reuse complex Claude prompts without writing code, differentiating from CLI-based prompt management tools
vs alternatives: Provides visual prompt template management with instant preview, making prompt engineering more accessible than text-based prompt files or command-line tools
Enables users to define multi-step workflows where Claude processes a task sequentially, with each step's output feeding into the next step's input. Implements a visual workflow builder (DAG-based) where users can chain Claude calls, conditional branches, and file operations. Handles state management across steps (storing intermediate results, passing context between steps) and provides execution logging with step-by-step progress tracking and error recovery.
Unique: Implements visual workflow orchestration with DAG-based execution directly in the browser, enabling non-technical users to chain Claude calls without writing code, differentiating from programmatic workflow tools like Zapier or Make that require backend infrastructure
vs alternatives: Provides visual workflow builder comparable to no-code automation platforms but optimized for Claude-specific tasks, with lower latency due to browser-native execution
+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 BrowserOS – "Claude Cowork" in the browser at 38/100.
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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