Approval Gate
Provides a human approval gate for AI agents, enabling interactive inline cards for approving, editing, or rejecting actions before they are executed.
README
Approval Gate — an MCP App
A human approval gate for AI agents, built as an MCP App. Before an agent does something consequential — send an email, issue a refund, publish a post, delete data — it calls a tool that renders an interactive card inline in the chat. A human clicks Approve, Edit, or Reject, and only then does the agent proceed.
It runs in any MCP Apps host: Claude (web/desktop), ChatGPT, VS Code, Goose, and others.
Why this and not a dashboard? The highest-leverage use of MCP Apps in real workflows isn't a fancy chart — it's the confirmation gate that stops an agent acting on a misread "yes". The artifact changes per client; the pattern doesn't.
How it works
MCP Apps extend MCP with one idea: a tool can declare a UI resource. When the tool is called, the host fetches that resource and renders it in a sandboxed iframe, then relays messages between the UI and your server.
This project wires up that pattern for an approval gate using three tools and one UI resource:
┌─────────────────────────────────────────────┐
agent calls │ request_approval (declares the UI resource)│
request_approval ───▶│ → creates a pending ApprovalRequest │
│ → host renders ui://approval-gate card │
└───────────────────────┬─────────────────────┘
│ card shows the artifact
▼
human clicks Approve / Edit / Reject
│
┌───────────────────────▼─────────────────────┐
the card calls │ submit_approval_decision │
(via the host) ─────▶│ → records the decision (once only) │
│ → returns the outcome to the agent │
└───────────────────────┬─────────────────────┘
│ "APPROVED" / "…WITH EDITS" / "REJECTED"
▼
agent proceeds with the action — or doesn't
list_approvals → read-only audit trail of every gate and its decision
The agent's contract is simple: call request_approval, then wait. It must not perform the real action until submit_approval_decision reports approved or edited. On edited, it uses the reviewer's corrected values; on rejected, it stops.
Project structure
mcp-approval-gate/
├── server.ts # MCP server: registers the 3 tools + the UI resource
├── main.ts # Entry point: stdio (--stdio) or Streamable HTTP
├── mcp-app.html # View shell (Vite bundles the React app into it)
├── src/
│ ├── types.ts # Shared, type-only model (ApprovalRequest, …)
│ ├── schemas.ts # Zod input schemas for the tools
│ ├── store.ts # In-memory store + audit trail (swap for a DB in prod)
│ └── ui/
│ ├── main.tsx # React entry
│ ├── useApprovalApp.ts# Hook around the ext-apps `App` class (connect/receive/submit/theme)
│ ├── ApprovalCard.tsx # The card: view / edit / reject / outcome states
│ └── styles.css # Theme-adaptive styling (matches host light/dark)
├── vite.config.ts # Bundles the View into a single self-contained HTML file
├── tsconfig.json # Typecheck for the View
├── tsconfig.server.json # Typecheck for the server
└── test-smoke.mjs # End-to-end runtime test over stdio
Prerequisites
- Node.js 20.19+ or 22+
- An MCP Apps-capable host to see the UI (Claude Desktop, ChatGPT, VS Code, Goose, …), or the
basic-hostfrom the ext-apps repo for local testing.
Setup
npm install
npm run build # typechecks, then bundles the View into dist/mcp-app.html
The server serves dist/mcp-app.html as the UI resource, so you must build before running.
Run
Streamable HTTP (default, on http://localhost:3001/mcp):
npm start
stdio (for Claude Desktop and other local hosts):
npm run start:stdio
During development, npm run dev rebuilds the View on change and restarts the server.
Use it in Claude Desktop
Build first (npm run build), then add this to your claude_desktop_config.json (use an absolute path):
{
"mcpServers": {
"approval-gate": {
"command": "npx",
"args": ["-y", "tsx", "/ABSOLUTE/PATH/TO/mcp-approval-gate/main.ts", "--stdio"]
}
}
}
Restart Claude Desktop, then try a prompt like:
Draft a reply to Jane approving her $40 refund, and ask me to approve it before sending.
The agent calls request_approval, the card appears inline, and your click decides what happens next.
Try it locally with basic-host
The ext-apps repo ships a reference host you can run against this server over HTTP:
# terminal 1 — this project
npm start
# terminal 2 — the reference host
git clone https://github.com/modelcontextprotocol/ext-apps.git
cd ext-apps && npm install && cd examples/basic-host && npm start
# open http://localhost:8080, point it at http://localhost:3001/mcp,
# call request_approval, and interact with the card
Test
test-smoke.mjs boots the server over stdio and drives the full flow — initialize, list tools, call request_approval, read the UI resource, submit an edited decision, verify the one-decision-only guard, and check the audit trail:
npm run build && node test-smoke.mjs
Adapting it
- Change the artifact, keep the gate.
request_approvaltakes a genericfields: [{ label, value, multiline? }]list, so the same card reviews an email, a refund, a social post, or a config change. Only the calling agent's prompt changes. - Make it durable. The store in
src/store.tsis an in-memoryMap— fine for a demo or single instance, lost on restart and not shared across replicas. Back it with Postgres or Redis for production; the function signatures (createRequest,recordDecision,listRequests) are the seam to replace. - Add structured output. Each tool currently returns text. Define an
outputSchemaand returnstructuredContentif your host consumes structured tool results.
Tech
TypeScript · React 19 · Vite (single-file bundle) · @modelcontextprotocol/ext-apps · @modelcontextprotocol/sdk. Built against the MCP Apps spec version 2026-01-26.
License
MIT — see 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。