"OpenAI Gym" matching MCP servers:

Lets an AI coding agent offload low-complexity, monotonous tasks to a cheap local or OpenAI-compatible model that runs its own server-side tool loop for reading, searching, and staging file writes, so large file contents and intermediate steps never enter the caller's context. Also provides summaries, extraction, classification, bulk file mapping, staged-diff review, model management, and session token-savings reporting.

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.

Provides tools to manage OpenAI API keys and spending through the OpenAI API. Requires an OpenAI admin API key for secure access to account management features.

Enables orchestration of OpenAI-compatible GLM and other model endpoints via MCP, supporting streaming, background runs, cancellation, and local run history, with an optional bridge to other MCP servers.

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.

Provides a standardized interface for interacting with OpenAI's tools and services through the Model Context Protocol, enabling seamless integration with MCP-compliant applications.

Connects AI assistants to a user's Codeforces account and live platform data, enabling problem discovery, submission review, rating tracking, contest planning, and friends or gym access through natural-language queries.

Exposes Gemini CLI's built-in tools and external MCP proxies through a unified MCP endpoint while providing an OpenAI-compatible API for Gemini models. It features configurable security modes to safely manage file system operations, web searches, and shell command execution.

Enables Claude to access OpenAI's chat models such as GPT-4o and o1 through natural language prompts.

MCP server for Antigravity IDE that registers local tools and provides an OpenAI-compatible API bridge with real-time database context.

Provides intelligent OpenAI API token management with automatic switching between model tiers when usage limits are reached. It enables users to track daily token consumption, estimate costs before making calls, and manage project-specific usage data.

Provides tools to interact with OpenAI API including chat completions, embeddings, content moderation, and file management through natural language.

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.

A Model Context Protocol server enabling AI assistants to generate images through OpenAI's DALL-E API with full support for all available options and fine-grained control.

A Model Context Protocol server implementation that enables connection between OpenAI APIs and MCP clients for coding assistance with features like CLI interaction, web API integration, and tool-based architecture.

Local MCP server that exposes fixed tools for GPT, Claude, and Gemini while routing to any OpenAI-compatible chat completions backend with independent configuration per target.

A versatile MCP server that enables natural language software development tasks using multiple LLM providers (OpenAI, Anthropic) with real-time visualization, cost management, and a comprehensive tool suite.

Lets MCP clients delegate private or bulk text tasks to a local OpenAI-compatible LLM while keeping data on the machine.

Enables Claude Code agents and subagents to get an independent read-only review of their changes from OpenAI Codex and to continue that same Codex conversation later for re-checks. Runs each request in an isolated CODEX_HOME so those threads stay separate from the Codex desktop app, supporting parallel callers and per-call model, effort, sandbox, and timeout settings.

Gives text-only LLM coding agents vision by routing images to a multimodal model and returning detailed textual descriptions. Supports local files, URLs, clipboard, base64, raw bytes, and multiple providers like OpenAI, Anthropic, and Gemini.

MCP server that completes prompts with automatic provider fallback across OpenAI, Anthropic Claude, and Google Gemini. It is rate-limit aware, retries on transient failures, and provides a transparent attempt log.

Gives OpenAI Codex behavioral history from workspace.json, exposing file fragility and co-change partners as tools to enforce safer edits and block incomplete patches.

Enables OpenAI Codex to consult Claude Code for co-analysis, adversarial second opinions, and read-only file review over the Model Context Protocol, with Claude remaining advisory and unable to modify files.

A secure mathematical computation sandbox that enables LLMs to perform symbolic math operations like algebra, calculus, and equation solving via SymPy. It features low-latency execution through pre-warmed process pools and provides standardized JSON outputs for reliable agent integration.

Read-only MCP server for GenieLocker that exposes tools for service status, commercial inventory, live quotes, recipe search, and credit pricing, without enabling purchases or model inference.

A tiny MCP server that wraps Perseus Vault to provide persistent, encrypted, local-first memory for OpenAI Codex agents, enabling them to remember project conventions, decisions, and context across sessions.

Exposes Z.ai GLM models (GLM-5.3 and siblings) as callable tools within Claude Code and Claude Desktop, enabling large-context second opinions, bulk grunt work, and independent model reasoning mid-conversation.

MCP server for exploring and integrating Toss Securities Open API, providing tools to browse endpoints, schemas, and generate code samples.

Turns an existing Express, Hono, Fastify, or Next.js API into a Streamable HTTP MCP endpoint, exposing one tool per OpenAPI operation that runs in-process through the app's existing routes, auth, validation, and handlers. It also serves discovery documents and readiness guidance so models can better understand and call the API.