OpenAI Codex MCP Server

Overview

The OpenAI Codex MCP Server bridges the powerful code‑generation and analysis capabilities of OpenAI’s Codex CLI with Claude through the Model Context Protocol (MCP). By exposing Codex as a set of MCP tools, developers can embed advanced programming assistance directly into their AI‑augmented workflows without needing to manage API keys or handle HTTP requests themselves. This server solves the common pain point of integrating external code tools into conversational AI agents: it provides a lightweight, language‑agnostic interface that translates MCP calls into Codex CLI commands and streams the results back to the assistant.

At its core, the server offers two primary tools. The tool is a versatile wrapper that gives Claude access to the full spectrum of Codex features—code generation, explanation, debugging, refactoring, security analysis, test creation, and documentation generation. It also supports multimodal input (images) and can invoke different AI providers behind the scenes, making it adaptable to a range of development environments. The tool operates in three automation modes—suggest, auto‑edit, and full‑auto—allowing developers to choose how much control they want over the generated code. The second tool, , launches an interactive Codex session, letting users chat with the model in a terminal‑style interface that can be useful for exploratory coding or quick prototyping.

The server supports both stdio and SSE (Server‑Sent Events) modes, enabling seamless integration with Claude Desktop or any web‑based client that can consume SSE streams. In stdio mode, the server runs as a subprocess of Claude Desktop, simplifying configuration and eliminating network overhead. In SSE mode, the server exposes HTTP endpoints ( for streaming and for sending prompts), making it straightforward to embed into custom web applications or CI/CD pipelines that already expose RESTful APIs.

Typical use cases include automated code reviews, on‑the‑fly bug fixes, generating unit tests from specifications, and producing documentation snippets—all triggered by natural language prompts within a conversation. For example, a developer can ask Claude to “Explain this function” or “Fix the bug in ”, and the MCP server will forward the request to Codex, stream back a refined answer, and optionally apply the changes automatically. This tight coupling reduces context switching between IDEs and chat interfaces, accelerates development cycles, and ensures that code changes are consistent with the assistant’s reasoning.

Unique advantages of this MCP server stem from its native multimodal support and multi‑provider flexibility, allowing teams to switch between different Codex or OpenAI models without changing the client code. The three‑level automation modes give developers granular control over how much the assistant should intervene, striking a balance between creative freedom and safety. By wrapping Codex as an MCP server, the solution stays future‑proof: any updates to the Codex CLI or new model releases automatically become available to all connected agents without requiring additional client-side updates.