ai-intervention-agent 1.5.9

AI Intervention Agent: MCP server enabling real-time user intervention in AI-assisted development workflows.

AI Intervention Agent

Real-time user intervention for MCP agents.

English | 简体中文

When using AI CLIs/IDEs, agents can drift from your intent. This project gives you a simple way to intervene at key moments, review context in a Web UI, and send your latest instructions via interactive_feedback so the agent can continue on track.

Works with Cursor, VS Code, Claude Code, Augment, Windsurf, Trae, and more.

Quick start

1. Install:

pip install ai-intervention-agent

# or
uv add ai-intervention-agent

2. Configure your AI tool to launch the MCP server via uvx:

{
  "mcpServers": {
    "ai-intervention-agent": {
      "command": "uvx",
      "args": ["ai-intervention-agent"],
      "timeout": 600,
      "autoApprove": ["interactive_feedback"]
    }
  }
}

[!NOTE] interactive_feedback is a long-running tool. Some clients have a hard request timeout, so the Web UI provides a countdown + auto re-submit option to keep sessions alive.

• Default: feedback.frontend_countdown=240 seconds
• Max: 250 seconds (to stay under common 300s hard timeouts)

3. (Optional) Customize your config:

• On first run, config.toml will be created under your OS user config directory (see docs/configuration.md).
• Example:

[web_ui]
port = 8080

[feedback]
frontend_countdown = 240
backend_max_wait = 600

Prompt snippet (copy/paste)

- Only ask me through the MCP `ai-intervention-agent` tool; do not ask directly in chat or ask for end-of-task confirmation in chat.
- If a tool call fails, keep asking again through `ai-intervention-agent` instead of making assumptions, until the tool call succeeds.

ai-intervention-agent usage details:

- If requirements are unclear, use `ai-intervention-agent` to ask for clarification with predefined options.
- If there are multiple approaches, use `ai-intervention-agent` to ask instead of deciding unilaterally.
- If a plan/strategy needs to change, use `ai-intervention-agent` to ask instead of deciding unilaterally.
- Before finishing a request, always ask for feedback via `ai-intervention-agent`.
- Do not end the conversation/request unless the user explicitly allows it via `ai-intervention-agent`.

Screenshots

_{Feedback page (auto switches between dark/light)}

More screenshots (empty state + settings)

_{Empty state (auto switches between dark/light)}

_{Settings (dark)}

Key features

• Real-time intervention: the agent pauses and waits for your input via interactive_feedback
• Web UI: Markdown, code highlighting, and math rendering
• Multi-task: tab switching with independent countdown timers
• Auto re-submit: keep sessions alive by auto-submitting at timeout
• Notifications: web / sound / system / Bark
• SSH-friendly: great with port forwarding

How it works

1. Your AI client calls the MCP tool interactive_feedback.
2. The MCP server ensures the Web UI process is running, then creates a task via HTTP (POST /api/tasks).
3. The browser (or VS Code Webview) renders tasks by polling the Web UI API.
4. When you submit feedback, the Web UI completes the task in the task queue.
5. The MCP server polls for completion (GET /api/tasks/{task_id}) and returns your feedback (text + images) back to the AI client.
6. Optionally, the MCP server triggers notifications (Bark / system / sound / web hints) based on your config.

VS Code extension (optional)

Item	Value
Purpose	Embed the interaction panel into VS Code’s sidebar to avoid switching to a browser.
Install (Open VSX)	Open VSX
Download VSIX (GitHub Release)	GitHub Releases
Setting	ai-intervention-agent.serverUrl (should match your Web UI URL, e.g. http://localhost:8080; you can change web_ui.port in config.toml.default)
Other settings	ai-intervention-agent.logLevel (Output → AI Intervention Agent) ai-intervention-agent.enableAppleScript (macOS only; for the “Run AppleScript” command; default: false. macOS native notifications are controlled separately and are enabled by default.)

Configuration

Item	Value
Docs (English)	docs/configuration.md
Docs (简体中文)	docs/configuration.zh-CN.md
Default template	config.toml.default (on first run it will be copied to config.toml)

OS	User config directory
Linux	~/.config/ai-intervention-agent/
macOS	~/Library/Application Support/ai-intervention-agent/
Windows	%APPDATA%/ai-intervention-agent/

Architecture

flowchart TD
  subgraph CLIENTS["AI clients"]
    AI_CLIENT["AI CLI / IDE<br/>(Cursor, VS Code, Claude Code, ...)"]
  end

  subgraph MCP_PROC["MCP server process (Python)"]
    MCP_SRV["ai-intervention-agent<br/>(server.py / FastMCP)"]
    MCP_TOOL["MCP tool<br/>interactive_feedback"]
    SVC_MGR["Service manager<br/>(ServiceManager)"]
    CFG_MGR_MCP["Config manager<br/>(config_manager.py)"]
    NOTIF_MGR["Notification manager<br/>(notification_manager.py)"]
    NOTIF_PROVIDERS["Providers<br/>(notification_providers.py)"]
    MCP_SRV --> MCP_TOOL
    MCP_SRV --> CFG_MGR_MCP
    MCP_SRV --> NOTIF_MGR
    NOTIF_MGR --> NOTIF_PROVIDERS
  end

  subgraph WEB_PROC["Web UI process (Python / Flask)"]
    WEB_SRV["Web UI service<br/>(web_ui.py / Flask)"]
    WEB_CFG_MGR["Config manager<br/>(config_manager.py)"]
    HTTP_API["HTTP API<br/>(/api/*)"]
    TASK_Q["Task queue<br/>(task_queue.py)"]
    WEB_FRONTEND["Browser frontend<br/>(static/js/app.js + multi_task.js)"]
    WEB_SRV --> HTTP_API
    WEB_SRV --> TASK_Q
    WEB_SRV --> WEB_CFG_MGR
    WEB_FRONTEND <-->|poll /api/tasks| HTTP_API
    WEB_FRONTEND -->|submit feedback| HTTP_API
  end

  subgraph VSCODE_PROC["VS Code extension (Node)"]
    VSCODE_EXT["Extension host<br/>(packages/vscode/extension.js)"]
    VSCODE_WEBVIEW["Webview frontend<br/>(webview.js + webview-ui.js<br/>+ webview-notify-core.js + webview-settings-ui.js)"]
    VSCODE_EXT --> VSCODE_WEBVIEW
    VSCODE_WEBVIEW <-->|poll /api/tasks| HTTP_API
    VSCODE_WEBVIEW -->|submit feedback| HTTP_API
  end

  subgraph USER_UI["User interfaces"]
    BROWSER["Browser<br/>(desktop/mobile)"]
    VSCODE["VS Code<br/>(sidebar panel)"]
    USER["User"]
  end

  CFG_FILE["config.toml<br/>(user config directory)"]

  AI_CLIENT -->|MCP call| MCP_TOOL
  MCP_TOOL -->|start/check Web UI| SVC_MGR
  SVC_MGR -->|spawn/monitor| WEB_SRV

  USER -->|input / click| WEB_FRONTEND
  USER -->|input / click| VSCODE_WEBVIEW
  BROWSER -->|load UI| WEB_FRONTEND
  VSCODE -->|render UI| VSCODE_WEBVIEW

  MCP_TOOL -->|"HTTP POST /api/tasks"| HTTP_API
  MCP_TOOL -->|"HTTP GET /api/tasks/{task_id}"| HTTP_API

  WEB_CFG_MGR <-->|read/write + watcher| CFG_FILE
  CFG_MGR_MCP <-->|read/write + watcher| CFG_FILE

  MCP_TOOL -->|trigger notifications| NOTIF_MGR
  NOTIF_PROVIDERS -->|system / sound / Bark / web hints| USER

Documentation

• API docs index: docs/api/index.md
• API docs (简体中文): docs/api.zh-CN/index.md
• MCP tool reference: docs/mcp_tools.md
• MCP 工具说明: docs/mcp_tools.zh-CN.md
• DeepWiki: deepwiki.com/xiadengma/ai-intervention-agent

Related projects

• interactive-feedback-mcp
• mcp-feedback-enhanced
• cunzhi
• other interactive-feedback-mcp

License

MIT License