human-loop-mcp
Enables AI agents to ask human operators questions through polished browser dialogs, supporting text/choice/confirmation inputs and file attachments.
README
<h1 align="center"> human-loop-mcp </h1>
<p align="center"> <strong>Browser-based human-in-the-loop MCP server</strong><br> Ask your AI agent to ask <em>you</em> — via polished browser dialogs. </p>
<p align="center"> <a href="#features">Features</a> · <a href="#install">Install</a> · <a href="#quick-start">Quick Start</a> · <a href="#tools">Tools</a> · <a href="#file-attachments">Attachments</a> · <a href="#configuration">Configuration</a> · <a href="#architecture">Architecture</a> · <a href="#development">Development</a> </p>
What Is This?
An MCP (Model Context Protocol) server that gives AI agents a way to ask the human operator questions through the browser.
- A lightweight localhost HTTP server spins up
- Your default browser opens a dialog page
- You fill in the form (optionally attaching files) and submit
- The response flows back to the agent through MCP
No Python, no Tkinter, no Electron — just a Node.js process, a browser tab, and clean HTML.
Why?
GUI-based human-in-the-loop tools often break on macOS (Accessibility permissions, focus stealing, Python framework builds). This project sidesteps all of that by using the browser as the UI layer — it works everywhere Node.js runs.
Features
| 7 MCP tools | Text input, multiline, choice, confirmation, info, health check, and usage guidance |
| File attachments | Drag-and-drop, paste, or click-to-upload files in the multiline dialog. Files are base64-encoded and returned alongside the text response (5 MB per-file limit) |
| Autocomplete | @ files, # GitHub issues/PRs, / slash commands, ? keyboard shortcuts |
| Dark/light mode | Follows system preference automatically |
| Keyboard-first | Cmd+Enter submit, Esc cancel, arrow-key navigation in dropdowns |
| Command-palette UI | Linear/Raycast-inspired design with purple accents, monospace labels, noise texture background |
| Zero bloat | Only two runtime dependencies: @modelcontextprotocol/sdk and zod |
| Single-file HTML | No static assets, no bundler — fully server-rendered |
Install
npm (recommended)
npm install -g human-loop-mcp
Or run directly without installing:
npx human-loop-mcp
From source
git clone https://github.com/dzulfiikar/human-loop-mcp.git
cd human-loop-mcp
npm install
npm run build
Quick Start
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"human-loop": {
"command": "npx",
"args": ["-y", "human-loop-mcp"]
}
}
}
Cursor / Windsurf
Add to .cursor/mcp.json or equivalent:
{
"mcpServers": {
"human-loop": {
"command": "npx",
"args": ["-y", "human-loop-mcp"]
}
}
}
GitHub Copilot CLI
[mcp_servers.human-loop]
command = "npx"
args = ["-y", "human-loop-mcp"]
From source (development)
[mcp_servers.human-loop-dev]
command = "npx"
args = ["tsx", "/path/to/human-loop-mcp/src/index.ts"]
Tools
| Tool | Description |
|---|---|
get_user_input |
Single-line text, password, integer, or float input |
get_user_choice |
Single or multi-select from a list of choices |
get_multiline_input |
Large text area with autocomplete and file attachments |
show_confirmation_dialog |
Binary confirm/cancel dialog |
show_info_message |
Informational message with acknowledgement |
get_human_loop_prompt |
Returns guidance text on when to use the tools |
health_check |
Returns server health status and available tools |
Example: Text Input
{
"title": "API Key",
"prompt": "Enter your OpenAI API key:",
"input_type": "password"
}
Example: Choice
{
"title": "Database",
"prompt": "Which database should I use?",
"choices": ["PostgreSQL", "MySQL", "SQLite"],
"allow_multiple": false
}
Example: Confirmation
{
"title": "Deploy",
"message": "Deploy v2.1.0 to production?",
"confirm_label": "Deploy",
"cancel_label": "Abort"
}
File Attachments
The get_multiline_input dialog supports file attachments through three methods:
| Method | How |
|---|---|
| Drag and drop | Drag files onto the textarea — a drop overlay appears |
| Paste | Cmd+V / Ctrl+V with an image or file in the clipboard |
| Upload button | Click the paperclip "Attach" button below the textarea |
Attachment behavior
- Any file type is accepted
- 5 MB per-file limit — oversized files are rejected with an inline error
- Files are base64-encoded and returned in the tool result alongside the text value
- Image previews are shown as thumbnails in the attachment chips
- Each chip shows the file name, size, and a remove button
- When no files are attached, the result is identical to the previous format (fully backward-compatible)
Result format
When attachments are present, the get_multiline_input tool returns:
{
"action": "submit",
"value": "Here is the screenshot",
"attachments": [
{
"name": "screenshot.png",
"type": "image/png",
"size": 48210,
"data": "iVBORw0KGgo..."
}
]
}
When no attachments are provided, the attachments field is omitted entirely.
Configuration
Environment variables
| Variable | Default | Description |
|---|---|---|
HITL_HOST |
127.0.0.1 |
HTTP server bind address |
HITL_PORT |
(ephemeral) | Fixed HTTP port. If unset, the OS assigns a random port |
HITL_NO_LAUNCH |
(unset) | Set to 1 to print dialog URLs to stderr instead of auto-opening the browser |
Programmatic usage
import { BrowserDialogServer } from "human-loop-mcp/browser/browser-dialog-server";
const server = new BrowserDialogServer({
port: 8080,
launchUrl: async (url) => {
console.log(`Open: ${url}`);
},
});
Architecture
┌──────────────────────────────────────────┐
│ MCP Client (AI Agent) │
│ Claude / Cursor / Copilot CLI │
└──────────────┬───────────────────────────┘
│ stdio (JSON-RPC)
┌──────────────▼───────────────────────────┐
│ index.ts Tool registration │
│ service.ts Business logic │
│ browser-dialog-server.ts HTTP + APIs │
│ dialog-session-manager.ts Sessions │
│ html.ts HTML/CSS/JS renderer │
└──────────────┬───────────────────────────┘
│ HTTP (localhost)
┌──────────────▼───────────────────────────┐
│ Browser Dialog UI │
│ Forms · Autocomplete · Attachments │
│ Dark/light · Keyboard shortcuts │
└──────────────────────────────────────────┘
Data flow
- MCP client calls a tool (e.g.
get_multiline_input) via stdio JSON-RPC index.tsroutes the call toHumanLoopServiceservice.tstransforms args and callsBrowserDialogServer.openDialog()browser-dialog-server.tscreates a session, starts HTTP if needed, opens the browserhtml.tsrenders a self-contained HTML page with the dialog form- User submits (with optional file attachments) — the POST handler validates and resolves the session promise
- Response flows back through MCP to the AI agent, including any base64-encoded attachments
Development
npm install # Install dependencies
npm run dev # Run from source (tsx, hot-reload)
npm run build # Compile to dist/
npm test # Run tests (Vitest)
npm run typecheck # Type-check without emitting
Project structure
src/
├── index.ts MCP server entry point & tool registration
├── service.ts Tool handler business logic
└── browser/
├── browser-dialog-server.ts HTTP server, browser launcher, API endpoints
├── dialog-session-manager.ts Session lifecycle, validation & attachment types
└── html.ts Dialog page renderer (forms, autocomplete, attachments)
tests/
├── service.test.ts
├── dialog-session-manager.test.ts
└── browser-dialog-server.test.ts
Contributing
See CONTRIBUTING.md for development setup, code style, and PR guidelines.
License
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。