Supabase ranks higher at 42/100 vs onestep-puppeteer-mcp-server at 25/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | onestep-puppeteer-mcp-server | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 25/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 |
| 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 to control headless Chrome/Chromium instances via standardized tool calls. Implements MCP server transport layer that translates LLM function-calling requests into Puppeteer API invocations, managing browser lifecycle (launch, navigation, interaction) and returning structured results back to the agent context.
Unique: Bridges Puppeteer directly into MCP protocol, enabling LLM agents to invoke browser automation as first-class tools without custom wrapper code. Implements MCP resource/tool discovery so agents can introspect available browser capabilities.
vs alternatives: Simpler integration than building custom Puppeteer API wrappers for each LLM framework; MCP standardization allows the same server to work with any MCP-compatible client (Claude, custom agents, etc.)
Implements Puppeteer navigation primitives (goto, reload, back, forward) with configurable wait conditions (networkidle, domcontentloaded, load) and returns full page content (HTML, text, metadata). Handles navigation timeouts, error states, and page load detection to ensure reliable content retrieval before proceeding with further automation steps.
Unique: Exposes Puppeteer's wait-condition logic through MCP, allowing agents to specify load-readiness criteria (networkidle, domcontentloaded) rather than fixed delays. Returns structured page metadata alongside content.
vs alternatives: More reliable than simple HTTP clients for JavaScript-heavy sites; wait conditions prevent race conditions where agent tries to extract data before page renders
Provides CSS/XPath selector-based element interaction (click, type, focus, hover) and element property retrieval (text, attributes, visibility). Uses Puppeteer's page.$(selector) and page.$$(selector) for element discovery, then invokes actions with error handling for missing/invisible elements. Returns interaction results and element state snapshots to the agent.
Unique: Wraps Puppeteer element APIs (page.$, page.$$, element.click, element.type) as discrete MCP tools, allowing agents to compose multi-step interactions. Includes element property introspection (text, attributes, visibility) for conditional branching.
vs alternatives: More granular than Selenium/Playwright wrappers that often batch operations; allows agents to inspect element state between actions for adaptive behavior
Captures full-page or viewport screenshots via Puppeteer's page.screenshot() with configurable options (format, quality, clip region). Returns images as base64-encoded strings or file paths, enabling agents to visually inspect page state or verify automation results. Supports full-page scrolling capture and region-specific screenshots.
Unique: Integrates Puppeteer screenshot capability into MCP, allowing agents to request visual snapshots as part of automation workflows. Supports both full-page and region-specific captures with configurable output formats.
vs alternatives: More flexible than static screenshot tools; agents can request screenshots at any point in a workflow to verify state or debug failures
Executes arbitrary JavaScript in the page context via Puppeteer's page.evaluate() and page.evaluateHandle(), returning serialized results. Enables agents to run custom scripts for data extraction, DOM manipulation, or state inspection without separate tool calls. Handles serialization of return values (primitives, objects, arrays) and error propagation.
Unique: Exposes Puppeteer's page.evaluate() as an MCP tool, allowing agents to execute arbitrary JavaScript in the page context. Handles serialization and error propagation transparently.
vs alternatives: More powerful than selector-based extraction for complex DOM structures; agents can run custom logic without leaving the browser context
Implements MCP server transport layer (stdio or HTTP) that exposes browser automation capabilities as discoverable tools and resources. Handles MCP protocol handshake, tool schema definition, and request/response marshaling. Allows MCP clients (Claude, custom agents) to discover available browser operations and invoke them with type-safe parameters.
Unique: Implements full MCP server stack (protocol handling, tool schema registration, request marshaling) for Puppeteer, abstracting away transport details. Enables seamless integration with any MCP-compatible client.
vs alternatives: Standardized MCP interface allows the same server to work with multiple clients (Claude, custom agents); avoids custom protocol/API design
Manages browser instance lifecycle (launch, close, context creation) through MCP tool calls. Handles browser initialization with configurable options (headless mode, viewport size, user agent) and graceful shutdown. Maintains single browser instance per server process with context isolation for multi-step workflows.
Unique: Exposes Puppeteer browser lifecycle as MCP tools, allowing agents to control browser startup/shutdown as part of workflows. Manages single persistent instance across multiple tool calls.
vs alternatives: Simpler than managing browser instances externally; agents can request browser operations without worrying about process management
Provides tools to get, set, and delete cookies and local storage via Puppeteer's page.cookies() and page.evaluate() APIs. Enables agents to persist authentication state, manage sessions, and handle cookie-based workflows. Supports cookie serialization/deserialization for cross-session reuse.
Unique: Wraps Puppeteer cookie APIs as MCP tools, enabling agents to manage session state as part of automation workflows. Supports cookie serialization for cross-session persistence.
vs alternatives: More convenient than manual HTTP header manipulation; agents can work with cookies at the browser level where they're naturally managed
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 onestep-puppeteer-mcp-server at 25/100.
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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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