AI Intervention Agent
This server provides a real-time human-in-the-loop intervention system for AI agents via a single MCP tool, interactive_feedback. Key capabilities include:
Request user feedback: Pause agent execution mid-task and present a Markdown-formatted message or question through a Web UI.
Predefined options: Offer single or multi-select choices to guide the agent with structured responses.
Text & image input: Collect free-form text and image uploads (returned as base64-encoded data) from the user.
Multi-task support: Manage multiple concurrent feedback requests, each with independent countdown timers.
Auto re-submit: Automatically re-submit to keep long-running sessions alive if the user doesn't respond in time.
Notifications: Alert users via web, sound, system notifications, or Bark (push notifications).
Broad compatibility: Integrates with Cursor, VS Code, Claude Code, Augment, Windsurf, Trae, and more.
VS Code extension: Optionally embed the feedback panel directly into the IDE sidebar.
Configurable: Customize the web UI port and feedback timeouts via a configuration file.
Uses Flask to provide a Web UI service with HTTP API endpoints for real-time user intervention and feedback management during AI agent interactions.
Renders Markdown content in the Web UI for displaying agent messages and user feedback with code highlighting and math rendering support.
Uses Mermaid for architecture diagrams in documentation to visualize the system's component relationships and data flow.
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., "@AI Intervention AgentI need to review the agent's current approach before it continues."
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.
Ever had your AI agent confidently walk off in the wrong direction mid-task? AI Intervention Agent gives you a Web UI to pause the agent at key moments, review what it's about to do, type a course-correction, attach screenshots, and resume — all through the MCP interactive_feedback tool, without ending the conversation.
Works with Cursor, VS Code, Claude Code, Augment, Windsurf, Trae, and more.
Quick start
Point your AI tool at the MCP server via uvx (installs and runs the latest version automatically):
{
"mcpServers": {
"ai-intervention-agent": {
"command": "uvx",
"args": ["ai-intervention-agent"],
"timeout": 600,
"autoApprove": ["interactive_feedback"]
}
}
}
Then add the prompt snippet below to your agent rules / system prompt, so the agent asks you through interactive_feedback instead of finishing tasks silently.
- 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`.Install the package (remember to pip install --upgrade ai-intervention-agent periodically):
pip install ai-intervention-agentThen configure your AI tool to launch the installed entry point:
{
"mcpServers": {
"ai-intervention-agent": {
"command": "ai-intervention-agent",
"args": [],
"timeout": 600,
"autoApprove": ["interactive_feedback"]
}
}
}If your IDE/CLI has an AI agent (Cursor, Claude Code, VS Code, Windsurf, Trae, Augment, ...), paste this prompt in chat and let it write the config:
Please configure my IDE / AI tool to use the `ai-intervention-agent` MCP server:
1. Locate the correct MCP config file for my current IDE
(e.g. `.cursor/mcp.json` or `~/.cursor/mcp.json` for Cursor,
`~/.claude.json` for Claude Code,
`.vscode/mcp.json` for VS Code).
2. Add this entry under `mcpServers`:
- command: `uvx`
- args: `["ai-intervention-agent"]`
- timeout: 600
- autoApprove: `["interactive_feedback"]`
3. Append the project's recommended prompt rules
(the "Prompt snippet (copy/paste)" block in this README)
to my agent rules / system prompt, so the agent always asks me
through `interactive_feedback` instead of ending tasks silently.
4. Verify by listing MCP servers and confirming `ai-intervention-agent` is loaded.interactive_feedback is a long-running tool; some clients enforce a hard request timeout. The Web UI ships a countdown + auto re-submit (feedback.frontend_countdown, default 240s, range 0 or [10, 3600]) to keep sessions alive — the default stays under the common 300s hard timeout.
Related MCP server: promptspeak-mcp-server
Screenshots
Key features
Real-time intervention — the agent pauses and waits for your input via
interactive_feedbackWeb UI — Markdown, code highlighting, and math rendering out of the box
Multi-task tabs — concurrent requests with independent countdowns, per-task draft autosave, and auto re-submit that keeps long sessions alive (your typed text and checked options are submitted at zero, never an empty prompt)
Typing-hold — the countdown auto-extends while you type and never fires mid-input (web page and VS Code extension alike)
Agent-loop ergonomics — per-task
header_labelcontext chips,question_type='yesno'one-click decisions, andfeedback_placeholderhintsNotifications — web / sound / system / Bark (iOS push), plus custom notification sound upload
SSH / LAN friendly — works behind port forwarding; mDNS publishes a
<host>.localURL when supportedi18n — Web UI + VS Code extension shipped in
en/zh-CN/zh-TWPWA, offline-aware, WCAG 2.1 AA accessible — installable from the browser, with contrast / focus / reduced-motion audited and locked by invariant tests
Stable install — built on Flask 3.x with conservative dependency pins; immune to the Starlette 1.0 breaking change that broke several MCP feedback servers in early 2026
Architecture overview
AIIA runs as a single Python process bridging three surfaces: an MCP
stdio server exposing interactive_feedback, a Flask web server with
an SSE event bus, and a persistent task queue feeding the notification
stack. The component diagram, the interaction and failure-recovery
sequence diagrams, the agent-side MCP parameter table, and the runtime
invariant catalogue live in docs/architecture.md.
VS Code extension (optional)
Embeds the interaction panel into VS Code's sidebar so you never switch to a browser.
Install: Open VSX, VS Code Marketplace, or download the VSIX from GitHub Releases
Key setting:
ai-intervention-agent.serverUrl— must match your Web UI URL (e.g.http://localhost:8080; change the port viaweb_ui.portinconfig.toml.default)More:
ai-intervention-agent.logLevel, macOS native notifications (on by default, toggle in the sidebar's Notification Settings panel) — full settings list and the AppleScript executor security model inpackages/vscode/README.md
Configuration
On first run, config.toml is created from config.toml.default in your OS user config directory — the full TOML reference is in docs/configuration.md:
OS | User config directory |
Linux |
|
macOS |
|
Windows |
|
For uvx, Docker, systemd, or SSH-remote runtimes where editing the file is awkward, the most-used web_ui settings can be overridden by env var at startup (invalid values log a WARNING and fall back safely; full surface in docs/configuration.md#environment-variable-overrides):
export AI_INTERVENTION_AGENT_WEB_UI_HOST=0.0.0.0 # default 127.0.0.1
export AI_INTERVENTION_AGENT_WEB_UI_PORT=8181 # default 8080, range [1, 65535]
export AI_INTERVENTION_AGENT_WEB_UI_LANGUAGE=en # auto / en / zh-CN / zh-TW
uvx ai-intervention-agentCLI inspection: --version, --help, and --print-config (dumps the effective merged config as jq-friendly JSON, with secret-like fields redacted — answers "is my port from env or from config.toml?" in one pipeline).
On iPhone, the smoothest setup wraps the Web UI in a Shortcuts automation and points Bark notification taps at it — step-by-step guide in docs/configuration.md#recommended-iphone-setup-shortcuts--bark.
Documentation
Docs index (by audience):
docs/README.md·docs/README.zh-CN.mdArchitecture (diagrams + agent workflow):
docs/architecture.mdMCP tool reference:
docs/mcp_tools.md·docs/mcp_tools.zh-CN.mdAPI docs:
docs/api/index.md·docs/api.zh-CN/index.mdTroubleshooting / FAQ:
docs/troubleshooting.md·docs/troubleshooting.zh-CN.mdRelease notes:
CHANGELOG.md· VS Code marketplace listing:packages/vscode/CHANGELOG.mdContributing:
CONTRIBUTING.md·CODE_OF_CONDUCT.md· scripts index:scripts/README.md· i18n guide:docs/i18n.mdRelease recovery runbook:
docs/release-recovery.md·docs/release-recovery.zh-CN.mdDeepWiki Q&A — AI-augmented Q&A over the repo:
Related projects
Project | Stars (approx.) | Focus |
mcp-feedback-enhanced (Minidoracat) | ~3.8k | Largest sibling; Web UI + Tauri desktop app, auto-command execution, SSH Remote / WSL detection. |
cunzhi (imhuso) | ~1.4k | Chinese-language project focused on preventing premature task completion. |
Relay (andeya) | new | Multi-IDE relay, multi-tab session merging, native desktop window, Cursor usage monitoring. |
new | Node.js port with WebSocket UI and Speech-to-Text via OpenAI Whisper. | |
interactive-feedback-mcp (junanchn) | ~50 | Win32-native always-on-top window, auto-reply rules. |
interactive-feedback-mcp (poliva) | ~310 | Direct ancestor fork (see Acknowledgements); minimal Python MCP, single feedback dialog. |
interactive-feedback-mcp (Pursue-LLL) | ~30 | Independent smaller-scale fork emphasising minimal dependencies. |
Where AIIA sits on the spectrum: AIIA targets the operationally deep end — Web UI + VS Code extension sharing one backend, production-grade observability (/metrics Prometheus endpoint + a reference Grafana dashboard), bilingual i18n + docs, strict invariant test discipline (8,200+ tests + 1,050+ subtests across 40 audit cycles), and a 5-job release pipeline. Want the smallest drop-in? poliva's fork. A desktop app? mcp-feedback-enhanced. Voice / multi-tab UI? Relay or the Node.js fork. Full-stack operational integration? AIIA.
Feature gap callouts (contributions welcome): Speech-to-Text input, always-on-top native window, Cursor usage monitoring, multi-tab session merging UI.
Star counts are approximate snapshots (last reviewed 2026-06); check each upstream for current numbers. Submit a PR if you'd like another related project listed.
Acknowledgements
This project's heritage traces back to Fábio Ferreira (2024) and Pau Oliva (2025), whose original noopstudios/interactive-feedback-mcp and poliva/interactive-feedback-mcp seeded the MCP interactive_feedback tool surface. Their copyright notices are preserved in LICENSE per the MIT license terms. The v1.5.x line is a substantial rewrite — Web UI, VS Code extension, i18n, notification stack, CI/CD pipeline — owned and maintained by @xiadengma (PyPI / Open VSX / VS Code Marketplace publisher).
License
MIT License
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