Codex Delegate MCP
The server acts as a bridge to delegate coding tasks to the OpenAI Codex CLI from AI assistants. It supports four modes:
Agent mode: multi-file implementation.
Plan mode: structured plan without code changes.
Ask mode: read-only Q&A over the codebase.
Review mode: code review over uncommitted changes, base branch diff, or commit SHA.
You can customize each delegation with model choice, reasoning effort, network access, timeout, and working directory. Resume previous threads with resumeThreadId. Cancel running delegations by delegation ID, thread ID, or all at once. Diagnose setup with the doctor tool (including deep checks). Run multiple tasks in parallel, with warnings if workspaces overlap. Output includes status, result, files edited, token usage, warnings, thread ID, and delegation ID.
Delegates coding tasks to the OpenAI Codex CLI, enabling agents to plan, implement, ask questions, and review code, with structured results, thread management, and cancellation.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Codex Delegate MCPRefactor auth module to async/await and add unit tests"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Codex Delegate
Stop burning your frontier agent's limits on boilerplate.
Delegate implementation to the OpenAI Codex CLI — your agent writes the brief and reviews the diff.
Use your best coding agent where its judgment matters most: understanding the task, shaping the plan, and reviewing the result.
Codex Delegate is the MCP bridge that lets Claude Code, Cursor, Copilot — or any MCP client — hand implementation to the OpenAI Codex CLI, then get a clean, structured result back for review.

🧠 Frontier quality, kept
Your assistant does what frontier models are actually for: understands the task, writes a precise brief, reviews the finished diff. Codex holds its own as the implementer — guided and checked by a smarter orchestrator. The result reads like frontier work, because a frontier model planned it and signed off on it.
Related MCP server: Universal Coder Bridge
⚡ Done faster
Codex tears through multi-file edits while a frontier chat model would still be streaming the first file. You delegate, keep working with your assistant, and the diff shows up done.
🔋 Your limits stop being the bottleneck
Delegated work runs on the OpenAI Codex CLI and its own usage — separate from your orchestrator's chat quota. Your Claude, Cursor, or Copilot subscription spends tokens on the brief and the review; Codex does the grinding. On API? That's the per-token grind moved off your main bill.


Features
📦 One result you can review — a compact JSON block: the final answer,
status, the files Codex's edit tools reported changing, per-turn token counts, and thethreadIdto continue from. Fields that carry no signal are omitted.📋 Plan first, then build it on the same thread —
planreturns schema-validated steps for you to approve, andagentimplements them.askanswers questions.reviewruns Codex's own reviewer over uncommitted work, a base branch, or a single commit.🧵 Resume — continue a Codex thread with
resumeThreadId.resumed: falsetells you the context did not carry over.🧑🤝🧑 Run several, cancel cleanly — the same question across models, or independent workers on independent directories.
cancelwaits for the exit and warns when a process outlives the kill deadline.🤝 One-command install — Claude Code and GitHub Copilot CLI take it as a plugin, with a skill that teaches your agent how to delegate well. Cursor, VS Code, JetBrains, Windsurf and Visual Studio add the stdio server in settings.
🩺
doctor— tells you exactly what's missing if setup isn't right.
Install
You need Node.js 20+ and the OpenAI Codex CLI, already logged in (codex login).
Claude Code
/plugin marketplace add andreilungeanu/codex-delegate-mcp
/plugin install codex-delegate@codex-delegate-mcpThen just ask:
Delegate to Codex: migrate src/api from callbacks to async/await and update the tests, then walk me through what changed.
That's the whole loop — Claude writes the brief, Codex grinds through the files, Claude walks you through the diff.
Cursor
Add an MCP server in Cursor Settings → MCP (or project .cursor/mcp.json):
{
"mcpServers": {
"codex-delegate": {
"command": "npx",
"args": ["-y", "codex-delegate-mcp"]
}
}
}Then ask Cursor to delegate implementation to Codex the same way.
GitHub Copilot CLI
copilot plugin install andreilungeanu/codex-delegate-mcpMore clients
{
"servers": {
"codex-delegate": {
"type": "stdio",
"command": "npx",
"args": ["-y", "codex-delegate-mcp"]
}
}
}Or run Chat: Install Plugin From Source with this repository's URL.
Under Settings → Tools → AI Assistant → Model Context Protocol (MCP), add a server with command npx and arguments -y codex-delegate-mcp.
{
"mcpServers": {
"codex-delegate": {
"command": "npx",
"args": ["-y", "codex-delegate-mcp"]
}
}
}Heads-up: Cascade caps you at 100 tools across all servers.
{
"servers": {
"codex-delegate": {
"type": "stdio",
"command": "npx",
"args": ["-y", "codex-delegate-mcp"]
}
}
}Requires 17.14+. Note the top-level key is servers, not mcpServers.
Kiro, Kilo Code, and any other MCP client
Add the following server to the client's MCP config:
{
"mcpServers": {
"codex-delegate": {
"command": "npx",
"args": ["-y", "codex-delegate-mcp"]
}
}
}MIT © Andrei Lungeanu
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Maintenance
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