| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Puppeteer browser automation capabilities through the Model Context Protocol (MCP), allowing LLM agents and tools to control headless Chrome/Chromium instances via standardized MCP tool calls. Implements a server that translates MCP function-calling schemas into Puppeteer API invocations, enabling remote browser control without direct library imports.
Unique: Wraps Puppeteer as an MCP server rather than a direct library, enabling LLM agents to invoke browser automation through standardized tool-calling protocols without managing browser lifecycle or connection pooling themselves
vs alternatives: Provides MCP-native browser automation (compatible with Claude and other MCP clients) whereas direct Puppeteer requires custom API wrappers and manual integration into LLM tool schemas
Implements MCP tools for navigating to URLs, waiting for page load completion, and extracting page content (HTML, text, metadata). Uses Puppeteer's page.goto() with configurable wait conditions (networkidle, load, domcontentloaded) and exposes page.content() and page.evaluate() for flexible content extraction.
Unique: Exposes Puppeteer's page.goto() and content extraction through MCP tool schemas with configurable wait conditions, allowing LLM agents to specify load strategies (networkidle vs domcontentloaded) without managing browser state directly
vs alternatives: More flexible than simple HTTP clients (handles JavaScript rendering) and more accessible than raw Puppeteer (no Node.js library dependency in the LLM client, works via MCP protocol)
Provides MCP tools for querying DOM elements via CSS selectors or XPath, clicking elements, filling form inputs, and extracting element properties. Implements Puppeteer's page.$(selector), page.$$(selector), and element.evaluate() patterns, with error handling for missing elements and stale references.
Unique: Wraps Puppeteer's element query and interaction methods (page.$, page.click, page.type) as discrete MCP tools, allowing LLM agents to compose multi-step interactions (find element → extract property → click → wait) without managing Puppeteer's page object
vs alternatives: More granular than Selenium (which requires explicit driver management) and more accessible than raw Puppeteer (no JavaScript knowledge required from LLM client, works via tool schemas)
Implements MCP tools for capturing full-page or viewport screenshots as PNG/JPEG, with options for clipping to specific regions or elements. Uses Puppeteer's page.screenshot() with configurable quality, format, and clip parameters, returning base64-encoded image data for transmission via MCP.
Unique: Exposes Puppeteer's screenshot capability through MCP with base64 encoding, enabling LLM vision models to analyze rendered page state without requiring direct image file access or external storage
vs alternatives: More efficient than HTTP-based screenshot APIs (no round-trip to external service) and more flexible than static HTML snapshots (captures actual rendered output including CSS, fonts, images)
Provides MCP tools for executing arbitrary JavaScript in the page context via page.evaluate(), allowing LLM agents to run custom scripts, extract computed properties, or trigger page-specific logic. Returns serialized JavaScript values (primitives, objects, arrays) with error handling for non-serializable results.
Unique: Exposes Puppeteer's page.evaluate() as an MCP tool, allowing LLM agents to execute arbitrary JavaScript without managing the Puppeteer page object or handling serialization/deserialization
vs alternatives: More powerful than DOM-only queries (can access JavaScript state and computed properties) but requires LLM to generate valid JavaScript, unlike higher-level tools that abstract away code generation
Implements MCP tools for waiting on page conditions (selector visibility, navigation completion, network idle, timeout-based delays) using Puppeteer's page.waitForSelector(), page.waitForNavigation(), and page.waitForFunction(). Enables LLM agents to synchronize browser state with automation logic without polling.
Unique: Exposes Puppeteer's wait primitives (waitForSelector, waitForNavigation, waitForFunction) as discrete MCP tools, allowing LLM agents to compose synchronization logic without managing Promise chains or async/await
vs alternatives: More reliable than fixed-delay sleeps (responds to actual page state changes) and more accessible than raw Puppeteer (no Promise or async JavaScript knowledge required from LLM client)
Provides MCP tools for getting, setting, and deleting cookies via page.cookies() and page.setCookie(), enabling session persistence and authentication workflows. Stores cookies in memory per browser instance or optionally persists to external storage for cross-session reuse.
Unique: Wraps Puppeteer's cookie management API as MCP tools, enabling LLM agents to handle authentication and session state without direct browser object access or manual cookie serialization
vs alternatives: More flexible than HTTP-only cookie handling (supports domain-specific cookies and attributes) but requires manual cookie management logic in the LLM agent (no automatic refresh or expiration handling)
Implements the MCP server protocol for browser automation, handling client connections, tool registration, and request/response serialization. Uses Node.js MCP SDK to expose Puppeteer capabilities as standardized MCP tools, with automatic browser instance creation and cleanup on client disconnect.
Unique: Implements the full MCP server protocol for Puppeteer, handling client lifecycle, tool schema registration, and request routing without requiring clients to manage browser state or Puppeteer dependencies
vs alternatives: Standardizes browser automation through MCP (compatible with Claude and other MCP clients) whereas custom REST APIs require client-specific integration code and lack tool discovery
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-mcp-server at 32/100.
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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