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Bridge AI coding hosts to the OpenAI Codex CLI for delegated implementation.
About
Bridge AI coding hosts to the OpenAI Codex CLI for delegated implementation.
Security Report
Codex Delegate is a well-structured MCP server that delegates code implementation tasks to the OpenAI Codex CLI. The code demonstrates good security practices with proper input validation, no hardcoded credentials, and appropriate use of environment variables for configuration. Permissions are well-scoped to the server's purpose (process spawning for Codex CLI, file I/O for workspace operations, and network access for the OpenAI API). Minor code quality observations exist but do not constitute security vulnerabilities. Supply chain analysis found 3 known vulnerabilities in dependencies (0 critical, 3 high severity). Package verification found 1 issue.
3 files analyzed · 7 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-andreilungeanu-codex-delegate-mcp": {
"args": [
"-y",
"codex-delegate-mcp"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
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.
⚡ 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-mcp
Then 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-mcp
More 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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