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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes OpenAI API endpoints (GPT-4, GPT-3.5, o1, etc.) as MCP tools callable directly from Claude or other MCP clients. Implements the Model Context Protocol server specification to translate MCP tool calls into OpenAI API requests, handling authentication, request marshaling, and response streaming back through the MCP transport layer. Enables seamless model-to-model composition without requiring the client to manage separate API credentials or HTTP clients.
Unique: Bridges OpenAI and Anthropic ecosystems via MCP protocol, allowing Claude to invoke OpenAI models as native tools without custom integration code. Implements full MCP server specification with streaming support, enabling bidirectional model composition.
vs alternatives: Unlike direct API switching or custom wrapper scripts, this MCP server maintains Claude's context and tool-calling semantics while transparently delegating to OpenAI, reducing context switching and enabling true multi-model orchestration.
Exposes configurable parameters for OpenAI API calls (model selection, temperature, max_tokens, top_p, frequency_penalty, presence_penalty, etc.) through MCP tool schema. Allows callers to specify model variant (GPT-4, GPT-3.5-turbo, o1, etc.) and fine-tune generation behavior per request without modifying server configuration. Parameters are validated against OpenAI API constraints and passed directly to the underlying API client.
Unique: Exposes OpenAI's full parameter surface through MCP tool schema, enabling per-request model and hyperparameter selection from Claude without server restart or configuration changes. Implements parameter validation and pass-through to OpenAI API.
vs alternatives: More flexible than static model selection (e.g., hardcoding GPT-4) and more ergonomic than managing separate API clients, allowing dynamic model switching within Claude's native tool-calling interface.
Implements streaming of OpenAI API responses through the MCP protocol, allowing large or real-time outputs to be transmitted incrementally rather than buffered entirely. Converts OpenAI's server-sent events (SSE) stream into MCP-compatible streaming responses, maintaining token-by-token delivery semantics while respecting MCP message framing. Enables low-latency perception of model outputs in Claude and other MCP clients.
Unique: Bridges OpenAI's server-sent events (SSE) streaming with MCP's streaming response protocol, enabling token-by-token delivery through the MCP transport layer. Handles backpressure and error recovery during streaming.
vs alternatives: Provides streaming semantics over MCP without requiring clients to manage separate WebSocket or SSE connections to OpenAI, maintaining unified MCP interface for both streaming and non-streaming requests.
Accepts OpenAI-compatible message arrays (with role, content, and optional function_calls fields) as input, enabling multi-turn conversations with full context history. Passes conversation state directly to OpenAI API without modification, allowing Claude to manage conversation context and delegate specific turns to OpenAI models. Supports system prompts, user messages, assistant responses, and tool/function call results in standard OpenAI format.
Unique: Transparently forwards OpenAI-compatible message arrays from Claude to OpenAI API, preserving full conversation context and system prompts. Enables Claude to orchestrate multi-turn conversations with OpenAI models without reformatting or context loss.
vs alternatives: Maintains OpenAI's native message format and context semantics, avoiding lossy translation layers that other wrappers introduce. Allows Claude to manage conversation state while delegating specific turns to OpenAI.
Exposes OpenAI's function calling API through MCP tool schema, allowing Claude to request that OpenAI models invoke specific functions or tools. Translates MCP tool definitions into OpenAI function_calls format, marshals function results back to OpenAI for follow-up reasoning, and handles the full function calling loop. Supports parallel function calls and automatic retry logic for failed invocations.
Unique: Implements full OpenAI function calling loop through MCP, translating between MCP tool definitions and OpenAI function_calls format. Handles multi-turn function calling with automatic result marshaling and follow-up reasoning.
vs alternatives: Enables OpenAI models to participate in tool-augmented reasoning workflows orchestrated by Claude, combining OpenAI's reasoning capabilities with Claude's tool-calling interface without manual schema translation.
Manages OpenAI API authentication by accepting and securely storing API keys (typically via environment variables or configuration). Injects credentials into all outbound OpenAI API requests without exposing them to the MCP client. Supports multiple authentication patterns (single key, key rotation, per-request key override) depending on deployment context.
Unique: Centralizes OpenAI API authentication at the MCP server level, preventing credential exposure to clients and enabling credential rotation without client changes. Implements standard environment variable-based credential injection.
vs alternatives: More secure than embedding API keys in client code or passing them through MCP messages. Enables credential isolation in multi-tenant deployments where different users may have different API quotas or keys.
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 OpenAI 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
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