AI CLI MCP Server

🇯🇵 日本語のREADMEはこちら

📦 Package Migration Notice: This package was formerly @mkxultra/claude-code-mcp and has been renamed to ai-cli-mcp to reflect its expanded support for multiple AI CLI tools.

An MCP (Model Context Protocol) server that allows running AI CLI tools (Claude, Codex, Gemini, Forge, and OpenCode) in background processes with automatic permission handling.

Did you notice that Cursor sometimes struggles with complex, multi-step edits or operations? This server, with its powerful unified run tool, enables multiple AI agents to handle your coding tasks more effectively.

Demo

Overview

This MCP server provides tools that can be used by LLMs to interact with AI CLI tools. When integrated with MCP clients, it allows LLMs to:

• Run Claude CLI with all permissions bypassed (using --dangerously-skip-permissions)
• Execute Codex CLI with approvals and sandbox bypassed (using --dangerously-bypass-approvals-and-sandbox)
• Execute Gemini CLI with automatic approval mode (using -y)
• Execute Forge CLI in non-interactive mode (using forge -C <workFolder> -p <prompt>)
• Execute OpenCode in non-interactive JSON mode (using opencode run --format json --dir <workFolder> <prompt>)
• Support multiple AI models: Claude (sonnet, sonnet[1m], opus, opusplan, fable, haiku), Codex (gpt-6-astra, gpt-5.4, gpt-5.6-sol, gpt-5.6-terra, gpt-5.6-luna, gpt-5.5, gpt-5.4-mini, gpt-5.3-codex, gpt-5.3-codex-spark, gpt-5.2), Gemini (gemini-2.5-pro, gemini-2.5-flash, gemini-3.1-pro-preview, gemini-3-pro-preview, gemini-3-flash-preview), Forge (forge), and OpenCode (opencode plus explicit oc-<provider/model> wrappers such as oc-openai/gpt-5.4)
• Manage background processes with PID tracking
• Parse and return structured outputs from both tools

Usage Example (Advanced Parallel Processing)

You can instruct your main agent to run multiple tasks in parallel like this:

Launch agents for the following 3 tasks using acm mcp run:

1. Refactor src/backend code using sonnet
2. Create unit tests for src/frontend using gpt-5.3-codex
3. Update docs in docs/ using gemini-2.5-pro

While they run, please update the TODO list. Once done, use the wait tool to wait for all completions and report the results together.

Usage Example (Context Caching & Sharing)

You can reuse heavy context (like large codebases) using session IDs to save costs while running multiple tasks.

1. First, use acm mcp run with opus to read all files in src/ and understand the project structure.
2. Use the wait tool to wait for completion and retrieve the session_id from the result.
3. Using that session_id, run the following two tasks in parallel with acm mcp run:
   • Create refactoring proposals for src/utils using sonnet
   • Add architecture documentation to README.md using gpt-5.3-codex
4. Finally, wait again to combine both results.

Benefits

• True Async Multitasking: Agent execution happens in the background, returning control immediately. The calling AI can proceed with the next task or invoke another agent without waiting for completion.
• CLI in CLI (Agent in Agent): Directly invoke powerful CLI tools like Claude Code or Codex from any MCP-supported IDE or CLI. This enables broader, more complex system operations and automation beyond host environment limitations.
• Freedom from Model/Provider Constraints: Freely select and combine the "strongest" or "most cost-effective" models from Claude, Codex (GPT), Gemini, and Forge without being tied to a specific ecosystem.

Prerequisites

The only prerequisite is that the AI CLI tools you want to use are locally installed and correctly configured.

• Claude Code: claude doctor passes, and execution with --dangerously-skip-permissions is approved (you must run it manually once to login and accept terms).
• Codex CLI (Optional): Installed and initial setup (login etc.) completed.
• Gemini CLI (Optional): Installed and initial setup (login etc.) completed.
• Forge CLI (Optional): Installed and initial setup completed.
• OpenCode (Optional): Installed and configured. This integration uses opencode run --format json, and explicit provider/model selection follows the oc-<provider/model> wrapper syntax exposed by ai-cli models.

Installation & Usage

There are now two primary ways to use this package:

• ai-cli-mcp: MCP server entrypoint
• ai-cli: human-facing CLI for background AI runs

MCP usage with npx

The recommended way to use the MCP server is via npx.

Using npx in your MCP configuration:

    "ai-cli-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "ai-cli-mcp@latest"
      ]
    },

Using Claude CLI mcp add command:

claude mcp add ai-cli '{"name":"ai-cli","command":"npx","args":["-y","ai-cli-mcp@latest"]}'

Human CLI usage with global install

If you want to use the production CLI directly from your shell, install the package globally:

npm install -g ai-cli-mcp

This exposes both commands:

• ai-cli
• ai-cli-mcp

Examples:

ai-cli doctor
ai-cli models
ai-cli run --cwd "$PWD" --model sonnet --prompt "summarize this repository"
ai-cli run --cwd "$PWD" --model opencode --prompt "summarize this repository with OpenCode defaults"
ai-cli run --cwd "$PWD" --model oc-openai/gpt-5.4 --session-id ses_123 --prompt "continue this session with an explicit OpenCode model"
ai-cli ps
ai-cli result 12345
ai-cli result 12345 --verbose
ai-cli peek 12345 --time 10
ai-cli wait 12345 --timeout 300
ai-cli wait 12345 --verbose
ai-cli kill 12345
ai-cli cleanup
ai-cli-mcp

Human CLI usage with npx

Because the published package name is still ai-cli-mcp, the shortest npx form for the CLI is:

npx -y --package ai-cli-mcp@latest ai-cli run --cwd "$PWD" --model sonnet --prompt "hello"
npx -y --package ai-cli-mcp@latest ai-cli run --cwd "$PWD" --model oc-openai/gpt-5.4 --prompt "hello from OpenCode"

Important First-Time Setup

For Claude CLI:

Before the MCP server can use Claude, you must first run the Claude CLI manually once with the --dangerously-skip-permissions flag, login and accept the terms.

npm install -g @anthropic-ai/claude-code
claude --dangerously-skip-permissions

Follow the prompts to accept. Once this is done, the MCP server will be able to use the flag non-interactively.

For Codex CLI:

For Codex, ensure you're logged in and have accepted any necessary terms:

codex login

For Gemini CLI:

For Gemini, ensure you're logged in and have configured your credentials:

gemini auth login

macOS might ask for folder permissions the first time any of these tools run. If the first run fails, subsequent runs should work.

CLI Commands

ai-cli currently supports:

• run
• ps
• result
• peek
• wait
• kill
• cleanup
• doctor
• models
• mcp

Example flow:

ai-cli doctor
ai-cli models
ai-cli run --cwd "$PWD" --model gpt-5.4 --prompt "use the default Codex model"
ai-cli run --cwd "$PWD" --model codex-ultra --prompt "fix failing tests"
ai-cli run --cwd "$PWD" --model opencode --session-id ses_existing --prompt "continue this OpenCode session"
ai-cli run --cwd "$PWD" --model oc-openai/gpt-5.4 --prompt "run with an explicit OpenCode backend model"
ai-cli ps
ai-cli peek 12345 --time 10
ai-cli peek 12345 12346 --time 10
ai-cli wait 12345
ai-cli wait 12345 --verbose
ai-cli result 12345
ai-cli result 12345 --verbose
ai-cli cleanup

run accepts --cwd as the primary working-directory flag and also accepts the older aliases --workFolder / --work-folder for compatibility.

OpenCode model selection accepts either:

• opencode for the CLI's configured default model
• oc-<provider/model> for an explicit OpenCode provider/model, for example oc-openai/gpt-5.4

ai-cli models exposes OpenCode machine-readably via opencode: ["opencode"] plus dynamicModelBackends.opencode, which points users to opencode models for backend-native discovery.

Codex model selection uses gpt-5.4 as the default advertised model.

doctor checks only binary availability and path resolution. Its JSON output includes a checks block that marks login state and terms acceptance as unchecked.

CLI State Storage

Background CLI runs are stored under:

~/.local/state/ai-cli/cwds/<normalized-cwd>/<pid>/

Each PID directory contains:

• meta.json
• stdout.log
• stderr.log
• exit-status.json for detached runs

Use ai-cli cleanup to remove completed and failed runs. Running processes are preserved.

Exit Status Tracking

Detached ai-cli runs persist natural exit status for all supported backends through exit-status.json. Non-zero exits are surfaced as failed with the recorded exitCode; zero exits are surfaced as completed with exitCode: 0. ai-cli kill records SIGTERM termination as a failed exit, and a tracked process that disappears without exit metadata is treated as failed rather than assumed successful.

Connecting to Your MCP Client

After setting up the server, add the configuration to your MCP client's settings file (e.g., mcp.json for Cursor, mcp_config.json for Windsurf).

If the file doesn't exist, create it and add the ai-cli-mcp configuration.

Tools Provided

This server exposes the following tools:

run