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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Puppeteer's headless Chrome/Chromium browser control through the Model Context Protocol, allowing LLM agents to programmatically navigate, interact with, and extract data from web pages. Implements MCP server transport layer that translates browser automation requests (navigation, clicking, form filling, screenshot capture) into Puppeteer API calls, enabling stateful browser sessions managed by the protocol's communication framework rather than direct library imports.
Unique: Implements browser automation as an MCP server primitive rather than a direct library, enabling LLM agents to control browsers through standardized protocol messages. This architecture decouples the browser lifecycle from the LLM client, allowing stateful automation workflows to persist across multiple protocol exchanges without re-initializing the browser.
vs alternatives: Unlike direct Puppeteer library usage in agent code, the MCP server pattern allows non-technical users to configure browser automation through Claude Desktop without writing JavaScript, while maintaining full Puppeteer capability access through the protocol layer.
Provides MCP-exposed methods for navigating to URLs, waiting for page load states, clicking elements, filling form fields, and triggering user interactions on web pages. Uses Puppeteer's Page API to manage navigation timeouts, wait conditions (networkidle, domcontentloaded), and interaction queueing, translating high-level user intents (e.g., 'click the login button') into precise browser automation sequences with error handling for stale elements and navigation failures.
Unique: Wraps Puppeteer's Page API within MCP's request-response protocol, enabling LLM agents to express navigation intents as structured messages rather than imperative code. The server handles page lifecycle management (navigation, wait conditions, error recovery) transparently, abstracting Puppeteer's asynchronous event model into synchronous MCP tool calls.
vs alternatives: More reliable than regex-based web scraping for interactive content because it uses a real browser engine with full JavaScript support; simpler than raw Puppeteer code for non-technical users because MCP abstracts connection management and error handling.
Extracts structured and unstructured content from rendered web pages through MCP tools that query the DOM, evaluate JavaScript, and capture page state. Implements methods to retrieve HTML content, extract text by selector, evaluate arbitrary JavaScript expressions in the page context, and capture full-page or element-specific screenshots, enabling LLM agents to analyze page content without direct browser API access.
Unique: Combines DOM querying, JavaScript evaluation, and screenshot capture into a unified MCP interface, allowing LLM agents to extract content in multiple formats (HTML, text, visual) without switching tools. The server manages the page context and JavaScript sandbox, preventing common issues like stale element references or context loss between calls.
vs alternatives: More flexible than static HTML scraping because it supports JavaScript evaluation and screenshot capture; safer than exposing raw Puppeteer to LLMs because the MCP server controls execution scope and resource limits.
Implements the Model Context Protocol server transport layer for Puppeteer, handling MCP message serialization, tool registration, request routing, and server lifecycle management. Uses the MCP SDK to expose browser automation capabilities as standardized tools with JSON schemas, managing the stdio or HTTP transport between MCP client and server, and coordinating browser process lifecycle (startup, shutdown, resource cleanup) with protocol session management.
Unique: Implements MCP server primitives (tool registration, message routing, transport handling) specifically for Puppeteer, abstracting the complexity of MCP protocol compliance from browser automation logic. The server pattern enables Puppeteer to be used as a composable tool within larger MCP ecosystems without requiring LLM clients to manage browser lifecycle.
vs alternatives: Cleaner integration with Claude Desktop and other MCP clients than embedding Puppeteer directly in client code; standardized tool schemas enable better LLM understanding of browser capabilities compared to ad-hoc function calling.
Manages browser context lifecycle, including page creation, cookie/session persistence, viewport configuration, and user agent customization through MCP tools. Implements context isolation where multiple pages can be managed within a single browser instance, with support for setting headers, cookies, and authentication tokens to simulate authenticated user sessions or specific client environments.
Unique: Abstracts Puppeteer's context and page management into MCP tools, enabling LLM agents to manage multiple browser pages and sessions through simple tool calls rather than imperative code. The server maintains context state across multiple MCP requests, enabling stateful workflows without explicit session tokens.
vs alternatives: More flexible than single-page automation because it supports multiple concurrent pages and session persistence; simpler than raw Puppeteer for managing authentication because the MCP server handles cookie and header management transparently.
Provides robust error handling and timeout management for browser automation operations, catching Puppeteer exceptions (navigation failures, element not found, timeout errors) and translating them into MCP error responses with diagnostic information. Implements configurable timeouts for navigation, element waiting, and JavaScript evaluation, with fallback behaviors for transient failures and clear error messages for LLM clients to understand failure modes.
Unique: Translates Puppeteer's asynchronous error model into synchronous MCP error responses, enabling LLM agents to understand and respond to automation failures without exception handling code. The server provides structured error information (error codes, diagnostic context) that LLMs can parse to make recovery decisions.
vs alternatives: More informative than silent failures because it provides detailed error context; more reliable than raw Puppeteer because the MCP server enforces timeouts and prevents hanging operations.
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 Puppeteer at 20/100.
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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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