| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Manages authenticated sessions to OpenAI's ChatGPT web interface by automating browser interactions through Puppeteer, handling login flows, session persistence, and token refresh cycles. Implements headless Chrome automation to bypass API rate limits and access ChatGPT without official API keys, storing session cookies and maintaining stateful connections across multiple conversation turns.
Unique: Uses Puppeteer-based browser automation to interact with ChatGPT's web interface directly, avoiding official API limitations and costs by automating the DOM interactions that a human user would perform, including handling CAPTCHA challenges and session persistence across requests.
vs alternatives: Provides free ChatGPT access without API keys or rate limits compared to official OpenAI API, but trades reliability and speed for cost savings and feature parity with the web interface.
Tracks multi-turn conversations by maintaining parentMessageId and conversationId references, enabling the library to reconstruct conversation threads and send follow-up messages in the correct context. Implements client-side conversation history tracking that maps message IDs to their parent messages, allowing the browser automation layer to inject the correct context when submitting new messages to ChatGPT.
Unique: Implements client-side conversation threading by tracking parentMessageId and conversationId pairs, allowing the library to reconstruct multi-turn conversations without relying on ChatGPT's internal conversation storage, enabling custom conversation logic and branching dialogue patterns.
vs alternatives: Provides explicit conversation state management compared to stateless API calls, enabling complex multi-turn interactions, but requires manual state persistence unlike official API which handles conversation storage server-side.
Maps ChatGPT web interface interactions to underlying API endpoints by analyzing network traffic and DOM structure, allowing the library to send requests directly to ChatGPT's backend services. Implements endpoint discovery and request/response serialization that mirrors ChatGPT's internal API contracts, including payload formatting, authentication headers, and response parsing without official API documentation.
Unique: Reverse-engineers ChatGPT's internal API by analyzing network requests and response formats, enabling direct API calls without browser automation overhead, but requires ongoing maintenance as OpenAI changes endpoint contracts without notice.
vs alternatives: Faster than pure browser automation (no DOM parsing overhead) but more fragile than official API since it depends on undocumented endpoints that change frequently without deprecation warnings.
Implements exponential backoff and retry mechanisms to handle transient failures in browser automation, including network timeouts, ChatGPT service unavailability, and DOM parsing errors. Detects specific error conditions (e.g., CAPTCHA challenges, session expiration, rate limiting) and applies targeted recovery strategies such as session refresh or request retry with exponential delays.
Unique: Implements error classification specific to ChatGPT's failure modes (CAPTCHA, rate limiting, session expiration) with targeted recovery strategies for each error type, rather than generic retry logic that treats all failures identically.
vs alternatives: More resilient than naive retry approaches by detecting specific error conditions and applying appropriate recovery strategies, but less robust than official API which has built-in rate limiting and error handling.
Provides TypeScript interfaces and types that model ChatGPT's request and response structures, enabling type-safe interactions with the reverse-engineered API. Defines types for conversation objects, message payloads, and API responses, allowing developers to catch type errors at compile time rather than runtime.
Unique: Provides comprehensive TypeScript types for ChatGPT's undocumented API, enabling type-safe interactions with a reverse-engineered service where official type definitions don't exist, improving developer experience despite the underlying API being unstable.
vs alternatives: Offers better IDE support and compile-time safety than JavaScript-only alternatives, but requires TypeScript compilation step and types may become stale if API changes.
Implements streaming response parsing to deliver ChatGPT responses incrementally as they arrive, rather than waiting for the complete response. Uses event-based callbacks or async iterators to emit partial messages as the browser receives them from ChatGPT, enabling real-time UI updates and reduced perceived latency in chat applications.
Unique: Implements streaming response parsing by intercepting browser network events and parsing ChatGPT's streaming response format, enabling real-time message delivery without waiting for complete response generation, a capability not available through official non-streaming API.
vs alternatives: Provides real-time response streaming similar to official OpenAI API streaming, but with higher latency and complexity due to browser automation overhead.
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 Unofficial API in JS/TS 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
+1 more capabilities