Codex Delegate MCP
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 MCP
Keep the brains. Delegate the build.
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: peer-cli-mcp
⚡ 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.
🔍 Results you can actually trust
An answer only counts as final if Codex exited cleanly and wrote its own last-message file. Cancel or time out a run and you still get the last thing Codex said — explicitly flagged as salvage, never passed off as finished work. And when Codex's tool calls fail inside a turn that otherwise looks clean, you get a warning saying so, because a confident summary of work that never happened is the expensive failure.
You → your agent (plans & reviews)
│ MCP delegate tool
▼
Codex CLI (implements)
│ edits your workspace
▼
Clean result: what changed, which files, the thread idFeatures
🤝 Native plugins — install into Claude Code, Cursor, or GitHub Copilot CLI and just say "delegate this to Codex". The shared skill teaches your agent how to delegate well.
📦 Clean, typed results — validated structured output: the final answer,
statusplus areasonwhen it isn'tcompleted,threadId, tokenusage, and the files Codex edited. Fields that carry no signal are omitted, so anything present is worth reading — and an emptywarningsgenuinely means a clean run.📋 Plan first —
planmode returns a schema-validated plan. Review it, then resume the same thread to implement it.💬 Ask anything —
askmode: read-only Q&A over your codebase, zero file changes.🕵️ Native code review —
reviewmode runs Codex's own reviewer over uncommitted work, a base branch, or a single commit.🧵 Resume — continue the same Codex thread with
resumeThreadId, and get told if the context didn't actually carry over.🛑 Cancel that means it — process-tree kill across platforms, and
cancelreturns once the process has ended, not once the kill was requested.📊 Token accounting — per-turn input, cached, output, and reasoning counts, straight from Codex.
🩺 Self-diagnosing — a
doctortool that tells you exactly what's missing if setup isn't right.🔌 Works everywhere MCP does — VS Code, JetBrains, Windsurf, Visual Studio, and more.
Quick start
You need Node.js 18+ and the OpenAI Codex CLI 0.144.0+, already logged in (codex login).
Claude Code
/plugin marketplace add andreilungeanu/codex-delegate-mcp
/plugin install codex-delegate-mcp@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-mcp": {
"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-mcp": {
"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-mcp": {
"command": "npx",
"args": ["-y", "codex-delegate-mcp"]
}
}
}Heads-up: Cascade caps you at 100 tools across all servers.
{
"servers": {
"codex-delegate-mcp": {
"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-mcp": {
"command": "npx",
"args": ["-y", "codex-delegate-mcp"]
}
}
}Good to know
This is a worker for an orchestrator host — not a replacement for Codex's first-party codex mcp-server. Your host writes the brief and reviews the diff; this bridge runs Codex with hooks disabled and your personal config ignored, then hands back evidence the host can trust. Treat the workspace as trusted: project .codex config still applies under Codex's normal precedence.
It works out of the box. Everything is tunable if you want it — models, reasoning effort, timeouts, Windows sandbox mode — in Configuration.
License
MIT © Andrei Lungeanu
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