Which integrations are available for this server?

Provides asynchronous job management for OpenAI Codex CLI, allowing long-running code generation and editing tasks without blocking, including start, poll, list, and cancel operations.

How do I use codex-async-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@codex-async-mcp Start codex job to change format to number_to_currency in prorate_calculation_service.rb" 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.

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codex-async-mcp

by benzkittisak

Overview Schema Related Servers Score Discussions

Python

Local

agent-async-mcp

Local MCP server that runs Codex, Cursor, and Gemini CLI tasks asynchronously — returns a job_id immediately instead of blocking, so the orchestrating agent never hits the MCP 60-second timeout.

How it works

Claude (orchestrator)
  │
  ├─ codex_start(prompt, cwd)  →  job_id (instant)
  │
  └─ codex_wait(job_id)        →  blocks up to 50 s, returns result
                                   loop again on timeout

A sequential queue ensures only one agent process runs at a time. Jobs are persisted in SQLite so the queue survives server restarts.

Related MCP server: agent-bridge-mcp

Install

curl -fsSL https://raw.githubusercontent.com/benzkittisak/claude-codex-mcp/master/install.sh | bash

The installer will:

Clone this repo to ~/.local/share/agent-async-mcp/
Create an isolated Python venv
Symlink agent-async to ~/.local/bin/
Detect Claude Code, Codex, Cursor, Claude Desktop and ask which to register

Uninstall:

curl -fsSL https://raw.githubusercontent.com/benzkittisak/claude-codex-mcp/master/install.sh | bash -s uninstall
# or, if already installed:
agent-async uninstall

CLI

agent-async list-agents              # show detected / registered agents
agent-async add-agent claude-code    # register with Claude Code CLI
agent-async add-agent codex          # register with Codex CLI
agent-async add-agent cursor         # register with Cursor IDE
agent-async add-agent claude-desktop # register with Claude Desktop
agent-async remove-agent <agent>     # unregister
agent-async status                   # open real-time job monitor
agent-async update                   # pull latest + reinstall
agent-async check-update             # check without installing
agent-async enable-auto-update       # schedule daily auto-update (09:00)
agent-async disable-auto-update      # remove scheduled auto-update
agent-async uninstall                # remove everything

Requirements

Python 3.11+
One or more agent CLIs: codex, cursor, gemini (optional — only needed for the tools you use)
Claude Code CLI (recommended orchestrator)

MCP Tools (13 total)

Codex

Tool	Description
`codex_start(prompt, cwd, approval_policy?, context_files?)`	Queue a Codex task → returns `job_id` instantly
`codex_wait(job_id, timeout_seconds=50)`	Block until done; loop on `{"status":"timeout"}`
`codex_await_any(timeout_seconds=50)`	Block until ANY queued job completes

Cursor

Tool	Description
`cursor_start(prompt, cwd, approval_policy?, context_files?)`	Queue a Cursor headless task → `job_id`
`cursor_wait(job_id, timeout_seconds=50)`	Block until done

Gemini

Tool	Description
`gemini_start(prompt, cwd, approval_policy?, context_files?)`	Queue a Gemini CLI task → `job_id`
`gemini_wait(job_id, timeout_seconds=50)`	Block until done
`gemini_confluence_start(title, cwd, ...)`	Ask Gemini to draft/publish a Confluence page
`gemini_pr_start(cwd, pr_goal, ...)`	Ask Gemini to draft/publish a PR

Shared / Queue

Tool	Description
`job_list(limit=20)`	List recent jobs (all agents), newest first
`job_cancel(job_id)`	Cancel running or pending job
`queue_status()`	`{"busy": bool, "pending_count": int}`
`agent_notify_done(job_id, summary?)`	Called BY an agent to signal completion

`approval_policy` values

Value	Behavior
`full-auto`	No prompts, no sandbox (use for automation)
`auto-edit`	Auto-applies edits
`suggest`	Read-only — pauses for interactive input (avoid in automation)

Permissions (settings.local.json)

Add to your Claude Code project's .claude/settings.local.json:

{
  "permissions": {
    "allow": [
      "mcp__agent-async__codex_start",   "mcp__agent-async__codex_wait",
      "mcp__agent-async__cursor_start",  "mcp__agent-async__cursor_wait",
      "mcp__agent-async__gemini_start",  "mcp__agent-async__gemini_wait",
      "mcp__agent-async__queue_status",  "mcp__agent-async__job_list",
      "mcp__agent-async__job_cancel",    "mcp__agent-async__agent_notify_done",
      "mcp__agent-async__codex_await_any"
    ]
  }
}

Usage pattern

# Start a job (returns immediately)
result = codex_start(
    prompt="In app/services/foo.rb line 42, change X to Y. Do not change anything else.",
    cwd="/path/to/repo",
    approval_policy="full-auto"
)
job_id = result["job_id"]

# Wait in a loop (each call blocks up to 50 s)
while True:
    result = codex_wait(job_id, timeout_seconds=50)
    if result["status"] == "timeout":
        continue
    break  # "done" | "error" | "cancelled"

Job data

Jobs are persisted in ~/.agent-async/:

~/.agent-async/
  queue.db          ← SQLite: job metadata, status, token usage
  jobs/<job_id>/
    output.txt      ← stdout + stderr from the agent process

Troubleshooting

`agent-async: command not found`

~/.local/bin not in PATH. Run:

source ~/.zshrc   # or ~/.bashrc

Or open a new terminal. The installer adds it automatically.

`status: "error"` immediately after `*_start`

The agent CLI failed to start. Check output:

cat ~/.agent-async/jobs/<job_id>/output.txt

Message	Fix
`command not found: codex`	Install codex: `npm install -g @openai/codex`
`command not found: gemini`	Install gemini CLI from github.com/google-gemini/gemini-cli
`permission denied`	`cwd` doesn't exist or is inaccessible

`status: "running"` forever

The subprocess is hung. Most common cause: approval_policy="suggest" waiting for interactive input. Always use "full-auto" for automation.

agent-async status   # open monitor to see live state

Cancel a stuck job:

job_cancel(job_id="<job_id>")

Old jobs filling up disk

find ~/.agent-async/jobs -maxdepth 1 -type d -mtime +7 -exec rm -rf {} +

Project structure

agent-async-mcp/
├── install.sh
├── mcp-monitor.py
├── pyproject.toml
└── src/
    └── agent_async_mcp/
        ├── server.py       # MCP entry point, tool definitions
        ├── job_manager.py  # queue, spawn, wait, cancel
        ├── db.py           # SQLite schema + helpers
        ├── config.py       # paths, timeouts, agent binaries
        └── cli.py          # agent-async CLI

Development

git clone https://github.com/benzkittisak/claude-codex-mcp
cd claude-codex-mcp
pip install -e ".[dev]"
pytest tests/ -v

Install Server

license - not found

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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