| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Creates and manages isolated browser sessions in Browserbase's cloud infrastructure, handling session initialization, configuration injection (cookies, viewport dimensions, context persistence), and graceful teardown. Sessions are managed through a stagehandStore that tracks active instances and enables multi-session parallel execution without local resource constraints.
Unique: Integrates Browserbase's cloud browser platform with Stagehand's LLM-driven automation, enabling session-level configuration injection (cookies, viewport, context persistence) at creation time rather than post-hoc, and manages sessions through a TypeScript stagehandStore that tracks lifecycle state across MCP tool invocations
vs alternatives: Eliminates local browser resource management and installation overhead compared to Puppeteer/Playwright, while providing LLM-native interaction patterns through Stagehand rather than raw API calls
Translates natural language instructions into precise web interactions (click, fill, submit) by leveraging Stagehand's LLM-powered DOM analysis and action execution. The system parses user intent, analyzes the current page DOM, generates atomic actions, and executes them against the cloud browser, with built-in retry logic for transient failures and visual feedback through annotated screenshots.
Unique: Stagehand integration provides LLM-native element selection and interaction without requiring developers to write selectors; the system uses vision-enabled DOM analysis to map natural language intent to atomic browser actions, with built-in retry logic and annotated visual feedback for debugging
vs alternatives: More resilient than selector-based automation (Puppeteer/Playwright) on dynamic sites, and more natural than raw API calls; comparable to Anthropic's computer-use but optimized for web-specific workflows and integrated with Browserbase cloud infrastructure
Exposes available browser automation tools and resources through MCP protocol introspection endpoints, enabling MCP clients (Claude Desktop, LLM frameworks) to discover capabilities, parameter schemas, and usage documentation without hardcoding tool definitions. The server implements MCP's tools_list and resources_list endpoints, providing JSON schemas for all browser automation operations.
Unique: Implements MCP protocol introspection endpoints (tools_list, resources_list) to enable dynamic tool discovery by MCP clients, eliminating need for manual tool configuration or hardcoded tool definitions; provides JSON schemas for all browser automation operations
vs alternatives: More discoverable than REST APIs without OpenAPI specs; enables automatic tool loading in MCP-compatible clients like Claude Desktop; comparable to other MCP servers but specifically optimized for browser automation tool schemas
Implements automatic retry logic for transient failures (element not visible, network timeouts, JavaScript errors) with exponential backoff and configurable retry limits, built into Stagehand's action execution layer. Failed interactions are automatically retried with increasing delays (100ms, 200ms, 400ms, etc.) up to a maximum number of attempts, with detailed error reporting for permanent failures.
Unique: Integrates Stagehand's built-in retry logic with exponential backoff at the action execution layer, automatically retrying transient failures (element not visible, timeouts) without requiring explicit retry code; provides detailed error context including retry count and final error for debugging
vs alternatives: More robust than single-attempt automation (Puppeteer/Playwright without custom retry logic); automatic retry logic eliminates need for manual wait/retry code; comparable to Selenium's implicit waits but with exponential backoff and LLM-aware error reporting
Captures full-page or viewport screenshots from the cloud browser and optionally annotates them with LLM-generated labels identifying interactive elements, form fields, and content regions. Annotations are overlaid on the screenshot to help LLMs understand page structure without requiring DOM parsing, enabling vision-based page analysis and debugging of automation workflows.
Unique: Integrates Stagehand's vision-enabled DOM analysis to generate semantic annotations (element type, purpose, interactivity) overlaid on screenshots, enabling LLMs to understand page structure visually without HTML parsing; annotations include bounding boxes and element labels for precise reference
vs alternatives: Richer than raw Puppeteer/Playwright screenshots (which are uninterpreted images); more efficient than full DOM serialization for LLM understanding, and provides visual debugging context that raw API responses cannot
Extracts structured data (JSON, CSV, tables) from web pages by leveraging LLM-powered content analysis to identify and parse relevant information without requiring predefined schemas or CSS selectors. The system analyzes page content, infers data structure, and returns normalized output, with support for multi-page extraction and pagination handling through Stagehand's automation capabilities.
Unique: Uses Stagehand's LLM-powered content analysis to infer data structure and extract information without predefined schemas or selectors; supports multi-page extraction with automatic pagination handling through natural language navigation commands, and returns normalized structured output (JSON/CSV)
vs alternatives: More flexible than selector-based scrapers (BeautifulSoup, Scrapy) for dynamic or poorly-structured sites; more maintainable than regex-based extraction; integrates pagination and JavaScript rendering natively through cloud browser automation
Supports dynamic selection of LLM providers (OpenAI, Anthropic Claude, Google Gemini, and compatible APIs) for driving web automation and content analysis, with configurable model names and automatic fallback routing if a provider is unavailable. Configuration is managed through CLI flags (--modelName) and environment variables, enabling runtime model switching without code changes.
Unique: Decouples LLM provider selection from core automation logic through CLI flags and environment variables, enabling runtime model switching without code changes; supports OpenAI, Anthropic, Google Gemini, and compatible APIs with provider-agnostic interface
vs alternatives: More flexible than single-provider solutions (e.g., Playwright with OpenAI only); comparable to LangChain's provider abstraction but optimized for web automation workflows and integrated directly into MCP server configuration
Provides advanced anti-detection capabilities through Browserbase's stealth mode and proxy support, configurable via CLI flags (--advancedStealth, --proxies) to mask automation signatures and evade bot detection. Stealth mode modifies browser fingerprints, disables detection APIs (navigator.webdriver), and rotates user agents, while proxy support enables geographic spoofing and IP rotation for compliance with regional restrictions.
Unique: Integrates Browserbase's native stealth mode and proxy infrastructure directly into MCP server configuration, enabling anti-detection at the cloud browser level rather than through client-side libraries; supports advanced fingerprint masking, navigator.webdriver disabling, and geographic IP rotation
vs alternatives: More comprehensive than client-side stealth libraries (puppeteer-extra-plugin-stealth) because it operates at the cloud browser infrastructure level; provides proxy support natively without requiring separate proxy management tools
+4 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.
Browserbase MCP Server scores higher at 60/100 vs Supabase at 42/100.
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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