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.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.