dsh-verify

dsh-verify

Browser acceptance testing for AI agent deliverables: verify agent output in real Chromium from JSON specs, with MCP server, visual regression, multi-browser support and GitHub Actions.

Category
Visit Server

README

dsh-verify

ci npm MCP server

You asked an AI to build a web app. It said "done." Does it actually work?

dsh-verify opens a real browser and checks — so you never have to take the agent's word for it.

dsh-verify in action

The quality gate for agent-built web apps. Works with any agent — DeepSeek Harness (dsh), Claude Code, Cursor, Copilot, Codex — and with any CI. You write what a human would check in a browser; a real browser executes it and returns a PASS/FAIL verdict with receipts (screenshots + diff images).

No LLM judges the outcome. The browser is the judge.


Why this exists

We ran a 4-agent web team (spec writer → frontend dev → QA → reviewer). Their own review said:

✅ "All requirements met. No issues found."

In a real browser, the dark-mode toggle did nothing — the .dark class was toggled, but the CSS rule was never written. Every agent self-test passed because there was nothing in the page for the agents to run. No one opened a real browser.

That's the gap: agents verify against what they believe they built, not against what a user actually experiences. Unit tests and static checks can't catch a missing CSS rule.

Build What the agents said What a real browser says
demo/buggy "No issues found" ❌ FAIL — background never changes
demo/fixed one CSS rule added ✅ PASS — theme flips

Same page. Same JS. One missing CSS rule. Two different verdicts.

Use it three ways

Entry point What it's for One-liner
MCP server Your AI agent verifies its own deliverable, mid-session claude mcp add dsh-verify -- npx -y -p dsh-verify dsh-verify-mcp
CLI You or your CI verify a build/URL npx dsh-verify --spec demo/fixed.json
GitHub Action Every push runs real-browser checks uses: 263311487-ux/dsh-verify/.github/actions/dsh-verify@main

From any AI agent (MCP)

claude mcp add dsh-verify -- npx -y -p dsh-verify dsh-verify-mcp

Then tell your agent, in plain words:

Verify http://localhost:3000 — click #dark-toggle, then check body background-color changed. Screenshot it.

Tools exposed: verify_spec (run a spec JSON), verify_url (inline checks, no files), generate_and_verify (the AI drafts the checklist, real Chromium executes it), health.

In CI (GitHub Action)

- uses: 263311487-ux/dsh-verify/.github/actions/dsh-verify@main
  with:
    spec: demo/fixed.json       # spec file or glob
    # url: https://staging.example.com   # optional override
    # out: dsh-verify-out               # report output dir (default)

The repo dogfoods it: the dogfood workflow asserts the fixed build passes and the buggy build fails on every push.

On the command line

npm install -g dsh-verify          # or: npx dsh-verify
npx playwright install chromium    # one-time browser download
npx dsh-verify --spec 'specs/*.json'
# [PASS] specs/home.json (5/5)
# [FAIL] specs/cart.json (4/5)
#   ❌ expect_text #total: got "0" want "99"

What's in the box

  • Deterministic judge — a real headless Chromium (or Firefox / WebKit) executes human-style checks: click, fill, text, classes, computed styles, URLs, console errors, network errors, pixels.
  • Receipts, not vibes — every run emits a self-contained HTML report with screenshots and red-highlighted diff images; --json for machines; exit 0/1 for CI.
  • Visual regression — screenshot baselines, pixel-diff with thresholds (expect_screenshot), refresh with --update-baselines.
  • AI-drafted checklists — dsh-verify gen --url ... --prompt "..." learns the page in a real browser, has an LLM draft the checklist, then executes it deterministically. The AI drafts; it never judges.
  • Multi-browser — chromium | firefox | webkit per spec or --browser.
  • Zero framework lock-in — a JSON spec is all there is. No config language, no SDK, no vendor.

Example spec

{
  "title": "my app",
  "serve": "dist",
  "browser": "chromium",
  "steps": [
    { "action": "goto", "path": "/index.html" },
    { "action": "click", "selector": "#count-btn", "count": 3 },
    { "action": "expect_text", "selector": "#count-btn", "text": "Clicked: 3" },
    { "action": "capture_style", "selector": "#page", "prop": "backgroundColor", "var": "bg_before" },
    { "action": "click", "selector": "#color-btn" },
    { "action": "expect_class", "selector": "#page", "class": "dark", "present": true },
    { "action": "expect_style_changed", "selector": "#page", "prop": "backgroundColor", "var": "bg_before" },
    { "action": "screenshot", "name": "final-state" }
  ]
}

Top-level fields: title, serve (static dir) or base (target URL), browser, steps. Run many at once with a glob; exit is 0 only if all pass.

The report

A self-contained HTML report — every step with a pass/fail badge, selector, and detail, plus screenshots:

dsh-verify report

Prove it (run it yourself)

git clone https://github.com/263311487-ux/dsh-verify && cd dsh-verify
npm install && npx playwright install chromium
npm run demo:fixed    # → PASS (11/11)
npm run demo:buggy    # → FAIL (exit 1) — the missing .dark rule, caught
npm test              # engine self-tests

The repo's own CI runs exactly that — engine self-tests, then asserts fixed passes and buggy fails — so the tool verifies itself on every push.

Agent Arena — can agents ship working web apps?

Same task, same prompt, same human checks — different agents, graded by dsh-verify in a real browser. Seed result (2026-08-17): 11/12 runs passed. The one failure: DeepSeek v4-flash single-shot shipped a todo app with 0 seeded todos (9/19 checks failed); with a real-browser self-check loop the same model passed 19/19.

Agent Arena

See docs/ARENA.md — methodology, the tasks, and how to run your own agent.

Badge your agent-built app

Built something with an AI agent? Prove it in a real browser and show the world:

[![agent deliverable: browser-verified](https://img.shields.io/badge/agent_deliverable-browser_verified-brightgreen?logo=playwright&logoColor=white)](https://github.com/263311487-ux/dsh-verify)

Add a spec, wire the GitHub Action, and the badge is earned, not claimed. See docs/verified-badge.md.

Roadmap

  • [x] MCP server · AI-drafted checklists · visual regression · multi-browser · GitHub Action · dsh plugin
  • [ ] Agent arena — a public benchmark: give the same task to different agent setups, grade them in real browsers, publish the leaderboard
  • [ ] Spec recorder (browser extension: click through once → spec generated)
  • [ ] Cloud runs + shareable report links + PR comments

License

MIT

Recommended Servers

playwright-mcp

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.

Official
Featured
TypeScript
Magic Component Platform (MCP)

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.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

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.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

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.

Official
Featured
TypeScript
Kagi MCP Server

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.

Official
Featured
Python
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Exa Search

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.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured