"OpenAI Gym" matching MCP servers:

An MCP server that provides deep knowledge about OpenAI APIs and SDKs, enabling users to query technical information through various MCP clients including ChatGPT Deep Research, Cursor, and OpenAI Responses API.

Enables semantic search, code exemplar lookup, knowledge-graph statistics, and cached retrieval over locally ingested notebook and Markdown content through MCP.

Enables Claude to directly invoke OpenAI's chat models (GPT-4o, GPT-4o-mini, o1-preview, o1-mini) through a Model Context Protocol integration, allowing users to query and compare responses from different AI models within Claude Desktop.

Enables interaction with OpenAI's Chat Completion and Assistants APIs, supporting assistant management, file operations, and direct queries to GPT models through standardized MCP tools.

A Model Context Protocol server that enables Claude users to access specialized OpenAI agents (web search, file search, computer actions) and a multi-agent orchestrator through the MCP protocol.

A lightweight bridge that wraps OpenAI's built-in tools (like web search and code interpreter) as Model Context Protocol servers, enabling their use with Claude and other MCP-compatible models.

An MCP server that retrieves and provides AI model information from OpenAI, Anthropic, and Google through a unified interface.

Production-ready MCP server that integrates OpenAI API with extensible tool support, enabling dynamic plugin loading and knowledge search capabilities through multiple interfaces including CLI and browser UI.

Enables integration with OpenAI models through the MCP protocol, supporting concise and detailed responses for use with Claude Desktop.

Enables interaction with OpenAI-compatible APIs (like Ollama) through MCP tools. Provides access to chat completions, model listings, and embeddings generation from local or remote OpenAI-style endpoints.

Provides semantic search and document retrieval via OpenAI Vector Store API, extending ChatGPT Deep Research with custom knowledge and tools.

An MCP server that provides expert knowledge on the OpenAI API, powered by RAG. It enables users to ask technical questions and get accurate answers, with support for search and fetch tools.

A minimal server/client application implementation utilizing the Model Context Protocol (MCP) and Azure OpenAI.

MCP server for document ingestion and semantic search on Qdrant. Enables ingesting local documents, generating embeddings with OpenAI, and performing vector search with metadata filters.

Provides access to OpenAI's advanced models (including o3) with integrated web search, Python code interpreter, and combined analysis capabilities. Enables users to perform web searches, execute code in sandboxed environments, and combine search with analysis through natural language.

Enables semantic search and management of documentation through vector similarity using Qdrant and Ollama/OpenAI embeddings.

Enables interaction with the CoinGecko Pro API to access cryptocurrency data including price history and market metrics through both MCP and OpenAI function calling.

MCP server that searches documents in Qdrant using embeddings from LMStudio. Takes a text query, converts it to a vector via LMStudio's OpenAI-compatible API, and performs semantic search in Qdrant.

A MCP server that exposes OpenAPI schema information to LLMs like Claude. This server allows an LLM to explore and understand large OpenAPI schemas through a set of specialized tools, without needing to load the whole schema into the context

A Python backend implementing the Model Context Protocol with Azure OpenAI integration, enabling applications to interact with LLMs through a standardized interface with streaming capabilities.