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 โ 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 โ or any MCP client โ hand implementation to the OpenAI Codex CLI, then get a clean, structured result back for review.
๐ง 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 โ 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
โก 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.
๐ Your limits stop being the bottleneck
Delegated work runs on the OpenAI Codex CLI and its own usage โ 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
๐ค Native plugins โ 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.
๐ฆ Clean, typed results โ one compact JSON block: the final answer,
status, the files Codex edited, and per-turn token counts. Fields that carry no signal are omitted, so anything present is worth reading.๐ Four modes โ
agentedits,planrequests a schema-validated plan for you to review and approve,askanswers questions, andreviewruns Codex's own reviewer over uncommitted work, a base branch, or a single commit.๐งต Resume โ continue the same Codex thread with
resumeThreadId, and get told if the context didn't actually carry over.๐งโ๐คโ๐ง Parallel, and cancellable for real โ fan a question across models or put independent workers on independent directories;
cancelreturns once the process has ended, not once the kill was requested.๐ฉบ Self-diagnosing โ a
doctortool that tells you exactly what's missing if setup isn't right.๐ Works everywhere MCP does โ VS Code, JetBrains, Windsurf, Visual Studio, and more.
The caveats are documented rather than buried: what an empty warnings does not prove, and when result is salvage instead of an answer, are in the delegate reference.
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 โ Claude writes the brief, Codex grinds through the files, Claude walks you through the diff.
Cursor
Add an MCP server in Cursor Settings โ 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 โ Tools โ AI Assistant โ 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 ยฉ Andrei Lungeanu
Configuration ยท Security ยท Privacy ยท Terms ยท Changelog
Maintenance
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