cowork-qa-mcp
Provides an LLM with a real Chromium browser to perform web tasks, recording every action into a structured trace for later verification of goal completion.
README
cowork-qa-mcp

A Model Context Protocol server that gives an LLM a real Chromium browser, records every action it takes toward a stated goal, and hands back a structured trace so the LLM (or a second LLM) can decide whether the goal was actually achieved.
Built on Playwright. Five tools, one binary, no cloud dependency.
Why
Most browser-tool MCP servers are stateless — the LLM clicks, gets HTML back, repeats. There's no record of what happened, no way to grade the run after the fact, and no goal context.
cowork-qa-mcp flips that:
- Every session starts with a goal in plain English.
- Every action (
goto,click,fill,press,eval) is recorded with timestamps, the URL after, and the page's aria-snapshot. - When the session ends, a JSON trace is persisted to disk and exposed via a single
qa_get_tracecall.
The orchestrating LLM can then reason over the trace ("did this run actually fulfill the goal, or did it click the wrong button?") instead of trusting the run-time chatter.
Tools
| Tool | What it does |
|---|---|
session_start |
Open a fresh tab, optional starting URL, return a session id |
session_act |
Run one of: goto, click, fill, press, eval. Records the step. |
session_observe |
Return current URL + full aria-snapshot of the page |
session_end |
Close the tab, persist the trace to disk, return the file path |
qa_get_trace |
Return the goal, every step, final URL, and final aria-snapshot — formatted for an LLM to read |
Install
Requires Node 20+. The package is on npm — no clone needed.
# Try it once, no install
npx cowork-qa-mcp
# Or install globally
npm install -g cowork-qa-mcp
The first install pulls Chromium via Playwright's postinstall (~150 MB).
Wire into your MCP-compatible client
Claude Code
claude mcp add cowork-qa --scope user -- npx -y cowork-qa-mcp
To watch the browser instead of running headless:
claude mcp add cowork-qa --scope user \
-e COWORK_QA_HEADED=1 \
-- npx -y cowork-qa-mcp
Verify with /mcp inside a fresh claude session — you should see cowork-qa ✓ connected and 5 tools.
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"cowork-qa": {
"command": "npx",
"args": ["-y", "cowork-qa-mcp"]
}
}
}
Cursor / Windsurf / other MCP clients
Any client that speaks the MCP stdio transport works. Point its server config at npx -y cowork-qa-mcp.
From source (for development)
git clone https://github.com/inSideos-designs/cowork-qa-mcp.git
cd cowork-qa-mcp
npm install
npm run build
node dist/server.js # stdio server, expects an MCP client
MCP Registry
This server is also published on the official MCP Server Registry as io.github.inSideos-designs/cowork-qa — clients that auto-discover from the registry will find it without any manual config.
Environment variables
| Variable | Default | Purpose |
|---|---|---|
COWORK_QA_HEADED |
unset (headless) | Set to 1 to launch Chromium with a visible window |
COWORK_QA_DATA |
<cwd>/.cowork-qa |
Directory where <session-id>.json traces are written |
Usage example
A typical end-to-end loop the orchestrating LLM runs:
session_start({ goal: "find the cheapest 14\" MacBook Pro on apple.com",
url: "https://www.apple.com/shop/buy-mac/macbook-pro" })
→ { session_id: "abc-123" }
session_observe({ session_id: "abc-123" })
→ URL + aria-snapshot
session_act({ session_id: "abc-123", action: "click",
target: "button:has-text('Continue')" })
# ... more acts / observes ...
session_end({ session_id: "abc-123" })
→ { steps: 7, trace_path: "~/.cowork-qa/abc-123.json" }
qa_get_trace({ session_id: "abc-123" })
→ Goal: ...
Steps (7 total): ...
Final URL: ...
Final aria-snapshot: ...
Trace format
Each trace is a JSON file:
{
"session_id": "abc-123",
"goal": "...",
"steps": [
{
"t": 142,
"action": "click",
"args": { "target": "...", "value": null },
"url_after": "...",
"aria_after": "..."
}
],
"final": { "url": "...", "aria": "..." },
"path": "/.../abc-123.json"
}
Limitations / known quirks
session_observecalls don't show up in the trace's step count — onlysession_actcalls do. The final aria-snapshot is captured atsession_end.evalruns the JS expression but doesn't return the value to the caller — only side effects on the page are observable.- One Chromium process is shared across all sessions in a server instance; each session gets its own context (cookies, etc. are isolated).
- Selectors are passed straight to Playwright. CSS, text-selectors (
button:has-text("Send")), androle=selectors all work.
License
MIT — see LICENSE.
Contributing
PRs welcome. Keep it small: this is meant to stay a thin, auditable server.
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。