Supabase ranks higher at 42/100 vs @iflow-mcp/mbadkins-puppeteer-plus-martech at 29/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | @iflow-mcp/mbadkins-puppeteer-plus-martech | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 29/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides remote-controlled headless Chrome/Chromium browser automation through the Model Context Protocol, enabling LLM agents to navigate websites, interact with DOM elements, and capture page states. Uses Puppeteer's CDP (Chrome DevTools Protocol) bindings to execute browser commands asynchronously, with MCP server wrapping exposing these as callable tools for Claude and other LLM clients.
Unique: Wraps Puppeteer's CDP bindings as an MCP server, allowing LLM agents to treat browser automation as a first-class tool with structured input/output schemas rather than requiring custom integration code
vs alternatives: Tighter LLM integration than standalone Puppeteer scripts because MCP standardizes tool discovery and parameter validation, reducing boilerplate for multi-step browser workflows
Analyzes loaded JavaScript, pixels, and HTTP requests on a webpage to identify installed marketing technologies (Google Analytics, Facebook Pixel, Adobe Analytics, TikTok Pixel, etc.). Implements pattern matching against known MarTech vendor signatures in script tags, global variables, and network requests, then returns structured inventory of detected tools with versions and configuration details.
Unique: Combines Puppeteer's network request interception with signature-based pattern matching for MarTech vendors, enabling simultaneous detection of script tags, global variables, and pixel beacons in a single page crawl
vs alternatives: More comprehensive than browser extensions like Wappalyzer because it captures network-level pixel fires and can be integrated into automated audit workflows via MCP, not just manual inspection
Extracts Google Analytics configuration parameters (tracking ID, property ID, custom dimensions, event tracking setup) by inspecting the gtag.js or analytics.js global objects and intercepting network payloads sent to Google's measurement servers. Parses query parameters from analytics requests to reconstruct the measurement configuration without requiring API credentials.
Unique: Reconstructs GA configuration from network payloads and global object inspection rather than relying on GA Reporting API, enabling audits without requiring GA Admin access or API credentials
vs alternatives: Faster and less credential-dependent than GA API queries for configuration validation, though less comprehensive than Admin API for historical data
Monitors and logs Facebook Pixel events fired during page interaction by intercepting fbq() calls and network requests to Facebook's conversion API endpoints. Captures event names, parameters, and user data (hashed email, phone, etc.) sent to Facebook, enabling validation of pixel implementation and event tracking accuracy without Facebook Business Manager access.
Unique: Intercepts both fbq() JavaScript calls and network requests to Facebook's conversion API, providing dual-layer visibility into pixel events that client-side inspection alone cannot achieve
vs alternatives: More transparent than Facebook Ads Manager for debugging pixel issues because it shows raw event payloads and network timing, not just aggregated conversion counts
Inspects Adobe Analytics (formerly Omniture) implementation by examining the AppMeasurement.js global object, tracking server configuration, and network requests to Adobe's data collection servers. Extracts report suite IDs, eVar/prop mappings, and event tracking setup to validate implementation accuracy and identify configuration drift.
Unique: Extracts Adobe Analytics configuration from AppMeasurement global object and network payloads, providing implementation-level visibility without requiring Adobe Analytics Admin Console credentials
vs alternatives: Enables offline auditing of Adobe implementations without Admin Console access, though less comprehensive than Admin API for historical reporting and processing rules
Monitors TikTok Pixel events by intercepting ttq.track() calls and network requests to TikTok's conversion API. Captures event names, parameters, and user identifiers (hashed email, phone, IP) sent to TikTok, enabling validation of pixel implementation and event tracking without TikTok Ads Manager access.
Unique: Dual-layer event capture combining ttq.track() JavaScript interception with TikTok API network request inspection, providing complete visibility into pixel event payloads
vs alternatives: More detailed than TikTok Ads Manager reporting for debugging because it shows raw event payloads and network timing before TikTok's server-side processing
Analyzes Google Tag Manager (GTM) and other tag manager containers by inspecting the dataLayer object, GTM configuration, and fired tags. Extracts container ID, deployed tags, triggers, and variables to validate tag manager setup and identify which tags are active on a given page.
Unique: Reconstructs GTM container state by inspecting dataLayer, window.google_tag_manager globals, and fired tag network requests, providing implementation-level visibility without GTM Admin API
vs alternatives: Enables real-time tag firing validation that GTM's preview mode cannot provide because it captures actual production tag execution and network payloads
Validates cross-domain tracking implementation by simulating navigation between domains and inspecting how user identifiers (cookies, first-party IDs, hashed emails) are passed across domain boundaries. Checks for proper cookie domain settings, URL parameter passing, and first-party data platform (CDP) integration for identity continuity.
Unique: Orchestrates multi-domain browser navigation to validate identity continuity, capturing cookie state and URL parameters across domain boundaries in a single automated workflow
vs alternatives: More comprehensive than manual testing because it systematically validates all identity passing mechanisms (cookies, URL params, CDP syncs) across multiple domains in one audit
+2 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 @iflow-mcp/mbadkins-puppeteer-plus-martech at 29/100.
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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