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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides a hierarchical directory interface organizing 87+ GPT-4-powered applications across 41+ categories (Legaltech, Sales, Chat Bots, Developer Tools, Autonomous AI Agents, etc.). Users navigate via category filters and view detailed product cards with links to external applications. The browsing experience is built on a curated taxonomy that maps use-case domains to specific tools, enabling non-technical users to find relevant applications without keyword search.
Unique: Organizes applications by 41+ domain-specific categories (Legaltech, Sales, Chat Bots, Developer Tools, Autonomous AI Agents) rather than generic AI tool classification, enabling vertical-specific discovery aligned to business use cases rather than technical capabilities.
vs alternatives: More focused on GPT-4 ecosystem than general AI directories like Product Hunt or Hugging Face, with domain-specific categorization that helps non-technical users find industry-relevant applications faster than keyword search.
Allows users to submit requests for new GPT-4 applications to be added to the directory. Submissions are collected and processed by the curation team, with a 'Requested' collection visible on the platform showing community-driven demand signals. This crowdsourced input mechanism feeds the directory's growth and helps identify gaps in the current 87-application catalog.
Unique: Implements a two-tier curation model: curated applications in the main directory plus a public 'Requested' collection showing community demand signals, creating transparency into what users want to see and enabling data-driven prioritization of additions.
vs alternatives: More transparent about community requests than closed directories like Product Hunt, allowing users to see what applications are being requested and vote with their submissions on what should be added next.
Maintains a 'Featured' collection of select GPT-4 applications given prominent visibility on the platform homepage or category pages. This editorial curation layer surfaces high-quality, innovative, or newly-launched applications above the full 87-application catalog. The mechanism for selection (editorial team, user votes, recency, quality metrics) is not documented but creates a discovery shortcut for users seeking the most relevant or innovative applications.
Unique: Implements editorial curation layer on top of the full directory, creating a 'best of' collection that surfaces high-impact applications without requiring users to browse all 87 entries, reducing discovery friction for time-constrained users.
vs alternatives: Provides curated recommendations similar to Product Hunt's 'Product of the Day' but specifically focused on GPT-4 applications, offering more targeted discovery than general AI tool directories.
Implements a 41+ category taxonomy mapping GPT-4 applications to business domains and use cases (Legaltech, Sales, Chat Bots, Developer Tools, Autonomous AI Agents, Customer Support, Content Creation, etc.). Each application is tagged with one or more categories, enabling users to filter and navigate by vertical or functional area. The taxonomy is fixed and curated by the platform team rather than user-generated, ensuring consistency and relevance.
Unique: Uses a domain-centric taxonomy (Legaltech, Sales, Chat Bots, Developer Tools, Autonomous AI Agents) rather than capability-centric categories (text generation, code generation, image generation), aligning discovery to business use cases and verticals rather than technical capabilities.
vs alternatives: More business-focused than technical AI directories like Hugging Face or Papers with Code, enabling non-technical users to find applications relevant to their industry without understanding underlying model capabilities.
Provides 'View details' links on each application card that navigate users to external product pages or landing sites. This capability acts as a bridge between the directory and the actual applications, enabling one-click access to full product information, pricing, sign-up flows, and documentation. The links are maintained as part of the application metadata and updated when products change URLs or shut down.
Unique: Implements a lightweight linking model that acts as a discovery funnel rather than a full product comparison tool — users navigate to external sites for detailed evaluation rather than comparing applications within the directory itself.
vs alternatives: Simpler and more maintainable than embedded product comparisons or reviews (like Product Hunt's detailed pages), but less sticky than platforms that keep users within the ecosystem for evaluation and comparison.
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 GPT-4 Demo at 18/100. Supabase also has a free tier, making it more accessible.
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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