MCP Test Runner

MCP Test Runner

AI 測試大師 — your AI QA loop, from analyze to advise.

English · 繁體中文

Universal MCP server for running tests across pytest / Jest / Cypress / Go,with built-in DOM analyzer, run history, and a self-improvement coach.

A Model Context Protocol server that lets Claude Desktop / Cursor / anyMCP client drive your test suite end-to-end: run tests, inspect failures(screenshot + video + trace), analyze a live URL to draft test cases, and —after each run — produce a prioritized action plan telling you exactly whatto fix or write next.

Full design notes: docs/framework.md.

What's in the box

• Run tests across multiple frameworks (web + mobile) via a single MCP surface
• Mobile via Maestro (since v0.3.0): same MCP tools, iOS Simulator /Android Emulator / real device; YAML flows; cross-platform without rewrites
• Failure artifacts: screenshot (base64-inlined), video, Playwrighttrace.zip / Maestro recordings
• Run history: every run snapshotted; HTML report shows a sparkline trend
• DOM / Screen analyzer — analyze_url for web (forms / nav / dialogs /CTAs + the API endpoints the page hits) and analyze_screen for mobile(maestro hierarchy → form / cta / tab_bar modules)
• Smart test generation (generate_test): hand it an analyzer moduleand it writes a runnable Playwright .py or Maestro .yaml with concreteselectors, not # TODO stubs
• Auto-retry flakes — pytest side via pytest-rerunfailures; Maestroside via custom retry wrapper (no native --reruns); flaky testssurfaced separately from real failures
• Self-improvement coach (get_optimization_plan): post-run analysisacross three lenses — suite quality, MCP usability, AI generation effectiveness
• JUnit XML output for CI integrations (GitHub Actions / Jenkins / GitLab)

Install

Two paths — pick the one that matches how you'll use it.

A. Run via uvx (zero install, recommended for end users)

Add mcp-test-runner to your client config without installing anything globally; uv fetches and runs it in an ephemeral environment per session:

{
  "mcpServers": {
    "mcp-test-runner": {
      "command": "uvx",
      "args": ["mcp-test-runner"],
      "env": { "QA_RUNNER": "pytest", "QA_PROJECT_ROOT": "/path/to/your-test-project" }
    }
  }
}

That's the whole setup. First call downloads the package; subsequent calls are cached. Switching versions: uvx [email protected] ....

B. Install into a project venv (for contributors / hacking)

pip install mcp-test-runner       # or: pip install -e . from a clone
playwright install                # only if you use pytest-playwright
pip install pytest-rerunfailures  # optional, enables auto-retry

Then point your client config at the same Python interpreter:

"command": "/path/to/.venv/bin/python",
"args": ["-m", "mcp_test_runner.server"]

Runner-specific prerequisites

Wire into Claude Desktop

Copy examples/configs/claude_desktop_config.example.json to:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Two environment variables drive the runtime:

Per-runner snippet

pytest-playwright:

"env": { "QA_RUNNER": "pytest", "QA_PROJECT_ROOT": "/path/to/python-project" }

Jest:

"env": { "QA_RUNNER": "jest", "QA_PROJECT_ROOT": "/path/to/node-project" }

Cypress:

"env": { "QA_RUNNER": "cypress", "QA_PROJECT_ROOT": "/path/to/cypress-project" }

Go test:

"env": { "QA_RUNNER": "go", "QA_PROJECT_ROOT": "/path/to/go-project" }

Other MCP clients

MCP is an open protocol — this server isn't Claude-only. The same Pythonprocess talks to any MCP client over JSON-RPC stdio. What differs acrossclients is (1) the config file format and (2) how reliably the underlyingmodel auto-chains tool calls.

Example configs ship in the repo:codex-config.example.toml ·gemini-config.example.json ·claude_desktop_config.example.json.

Codex (TOML):

[mcp_servers.mcp-test-runner]
command = "/path/to/.venv/bin/python"
args = ["-m", "mcp_test_runner.server"]
cwd = "/path/to/mcp-test-runner"
[mcp_servers.mcp-test-runner.env]
QA_RUNNER = "pytest"
QA_PROJECT_ROOT = "/path/to/your-test-project"

Gemini (JSON, same shape as Claude Desktop):

{
  "mcpServers": {
    "mcp-test-runner": {
      "command": "/path/to/.venv/bin/python",
      "args": ["-m", "mcp_test_runner.server"],
      "cwd": "/path/to/mcp-test-runner",
      "env": {
        "QA_RUNNER": "pytest",
        "QA_PROJECT_ROOT": "/path/to/your-test-project"
      }
    }
  }
}

Tool descriptions already nudge the recommended chains(analyze_url → generate_test, get_qa_context before generatingdomain tests). Clients with weaker tool-selection benefit most fromexplicit prompts that name the steps.

Tool surface

Shared across all runners (some tools degrade gracefully on non-pytest runners):

Resources

Self-improvement loop

After every run, _archive_report() snapshots report.json intotest-results/history/ and writes a fresh optimization-plan.md covering:

1. Suite quality — outcomes string per test (PFPFP); transitions → flakescore; 3+ identical-signature fails → broken; rerun-passed → flaky-in-run
2. MCP usability — top tools, error rates, repeat-arg patterns, commonA→B chains (from telemetry JSONL logs)
3. AI strategy — adoption rate of generate_test outputs, coverage gapsfrom analyze_url modules with no matching test files

The plan emits prioritized actions (high / medium / low) each withtarget + evidence + suggestion + optional auto_action_hint the MCP clientcan chain into the next tool call.

Project layout

mcp-test-runner/
├── pyproject.toml
├── src/mcp_test_runner/
│   ├── server.py            # MCP entry (tool routing + telemetry wrap)
│   ├── config.py            # Paths + env vars
│   ├── runners/             # Per-framework plugins
│   │   ├── base.py          # TestRunner abstract interface
│   │   ├── pytest_playwright.py
│   │   ├── jest.py
│   │   ├── cypress.py
│   │   └── go_test.py
│   ├── reporters/
│   │   └── html.py          # Self-contained HTML render
│   └── tools/               # Thin shims + analyzer + optimizer + telemetry
└── tests_project/           # Example project under test

Adding a runner

1. Create src/mcp_test_runner/runners/your_runner.py, subclass TestRunner,implement the abstract methods
2. Register the name in runners/__init__.py's REGISTRY
3. Done

End-to-end workflow

The intended pipeline — from a URL to "what should I improve next time":

flowchart LR
    URL[URL] -->|analyze_url| MOD[modules<br/>+ candidate TCs<br/>+ API endpoints]
    MOD -->|generate_test<br/>module=...| TEST[tests/test_*.py<br/>runnable skeleton]
    TEST -->|run_tests| RES[report.json<br/>+ screenshots<br/>+ trace.zip<br/>+ junit.xml]
    RES -->|auto archive| HIST[history/ snapshot]
    RES -->|generate_html_report| HTML[HTML report<br/>self-contained]
    HIST -->|auto write| PLAN[optimization-plan.md]
    PLAN -.->|next session reads| URL

The loop is the point: every run feeds the optimizer, the optimizerpoints at the weakest link, the next run hits that link first.

Walkthrough — testing a login page

In a Claude / Cursor session:

You: 分析 https://shop.example/login，幫我寫對應測試

Claude: [analyze_url] Found 1 form (email_password_form_0) + 3 APIendpoints. 5 candidate TCs.[generate_test with the form module] Wrote tests/test_login.py —runnable with concrete selectors, no # TODO stubs.

You: 跑

Claude: [run_tests] 23 passed, 0 failed in 31s. Screenshots + steptraces captured for every test.

You: 下一步該做什麼？

Claude: [opens report://optimization]Top: tests/test_login.py::test_invalid_credentials is flaky(flake_score=0.4, outcomes=PFPFP). Suggestion: addwait_for_response('/api/login') before asserting the error message.

The three optimizer lenses (suite quality / MCP usability / AI generationeffectiveness) make every "下一步" answer data-driven, not gut feel.

Walkthrough — testing a mobile app (Maestro)

Same shape, different runner. Requires Maestro CLI installed +simulator/emulator booted + your app launched (or pass launch_app=truewith app_id):

You: 分析 your mobile app 首頁的條碼按鈕、寫對應測試

Claude: [analyze_screen(app_id="com.example.app", launch_app=true)]Found 15 interactive elements; matched barcode_button(text="條碼", resource_id="barcodeButton") + candidate TCs.[generate_test with the cta module] Wrote maestro-flows/test_barcode.yaml —tapOn: { id: barcodeButton } + waitForAnimationToEnd + takeScreenshot,ready to maestro test.

You: 跑

Claude: [run_tests] 5 flows pass, retry didn't fire. Screenshotsembedded in HTML report.

You: 上面這個按鈕有時候會 fail、為什麼？

Claude: [get_optimization_plan] barcode_button::barcode_button flaggedflaky (flake_score=0.4, outcomes=PFPFP, rerun_count=1). Suggestion: 加waitForAnimationToEnd 或 extendedWaitUntil 等動畫穩定後再 tap。

Mobile-specific notes:

• The same qa-knowledge.md (built-in methodology + your domain) feedsboth web and mobile runs — write your business rules once.
• analyze_screen filters out iOS status bar (signal / wifi / battery)and asset-name labels (bg_*, *_filled); the result is signal-heavy.
• Maestro's takeScreenshot: <name> directive controls which screensshow up as inline images in the HTML report.

Prompting cookbook

Each row shows a phrase you can paste into a Claude / Cursor session andthe underlying MCP tool call it should trigger. Use as a reference for"how do I get the AI to do X without naming the tool myself."

One-time setup

Day-to-day testing

Building tests from a URL (web)

Building tests from a mobile screen (Maestro)

Requires QA_RUNNER=maestro, Maestro CLI, and a booted simulator/emulator/device.

BlueStacks / remote Android instances: set QA_ANDROID_HOST=127.0.0.1:5555(or whatever host:port BlueStacks exposes — see Settings → Advanced → AndroidDebug Bridge). The Maestro runner will adb connect before each test andanalyze_screen, and bumps the hierarchy timeout to 60s to absorb theslower TCP-ADB path. Genymotion / Nox / LDPlayer / WSA work the same way;any host:port that responds to adb connect is fine.

Continuous improvement

Tips — getting Claude to pick the right tool

• Mention QA knowledge explicitly — "reference qa knowledge when testing coupon" pushes Claude to call get_qa_context first; saying just "test coupon" may skip it.
• State the order — "analyze first, then write" forces analyze_url before generate_test; "just write a test for X" skips analysis.
• Batch vs precise — "auto-generate the whole page" → auto_generate_tests; "write one test per candidate_tc" → manual chain.
• Failure debugging — Asking "why did it fail / show me the screenshot" reliably triggers get_failure_details (which now returns screenshot + trace + video paths).

Anti-patterns

• ❌ "Run it 5 times to see if it's flaky" — the runner has auto-retry + history; just ask "is it flaky" and let get_optimization_plan answer.
• ❌ "Generate 100 tests" — noise > signal. Use get_optimization_plan first to find what's missing.
• ❌ "Test all edge cases" — too vague. Phrase as "test every candidate_tc for this form" — concrete, bounded, traceable.

Sample outputs

analyze_url (excerpt)

{
  "url": "https://shop.example/login",
  "page_title": "Login",
  "module_count": 3,
  "modules": [
    {
      "kind": "form",
      "name": "email_password_form_0",
      "selectors": {
        "container": "#login",
        "fields": [
          {"label": "Email", "selector": "#email", "type": "email", "required": true},
          {"label": "Password", "selector": "#password", "type": "password", "required": true}
        ],
        "submit": "button[type='submit']"
      },
      "candidate_tcs": [
        "所有必填欄位為空時送出，應顯示必填錯誤",
        "Email 欄位填入格式錯誤的字串（無 @），應顯示格式錯誤",
        "Password 欄位輸入後應預設遮蔽（type=password）",
        "全部填入合法值後送出，應觸發成功流程"
      ]
    }
  ],
  "api_endpoints": [
    {
      "method": "POST",
      "path": "/api/login",
      "status": 401,
      "candidate_tcs": [
        "POST /api/login payload 缺必填欄位應回 400 + 欄位錯誤訊息",
        "POST /api/login 合法 payload 應回 2xx",
        "POST /api/login 缺少 auth header 應回 401/403"
      ]
    }
  ]
}

generate_test output (smart, with module)

"""
Login happy path

Auto-generated from analyze_url module: email_password_form_0 (kind=form)
"""
from playwright.sync_api import Page, expect


def test_login(page: Page):
    page.goto('https://shop.example/login')
    page.locator('#email').fill('[email protected]')
    page.locator('#password').fill('TestPass123!')
    page.locator("button[type='submit']").click()
    # TC: Email 欄位填入格式錯誤的字串（無 @），應顯示格式錯誤
    # TC: Password 欄位輸入後應預設遮蔽
    # TC: 正確 Email + 正確密碼 → 導向 dashboard
    # TODO: 補上實際斷言，例如：
    # expect(page).to_have_url(...)
    # expect(page.get_by_text("成功")).to_be_visible()

optimization-plan.md (excerpt)

# Optimization Plan — 2026-05-12T14:03:40

_Based on 6 archived runs._

## Prioritized Actions

### 1. 🔴 HIGH — flaky
- **Target**: `tests/test_login.py::test_invalid_credentials`
- **Evidence**: flake_score=0.4, outcomes=PFPFP, rerun_count=1
- **Suggestion**: 加 explicit wait（wait_for_response / locator wait）

### 2. 🟡 MEDIUM — coverage_gap
- **Target**: `register_form`
- **Evidence**: 由 analyze_url 偵測但 repo 內找不到對應 test_*.py
- **Suggestion**: `call generate_test(description="...", filename="test_register_form.py")`

HTML report

Open the live rendered demo →(served via GitHub Pages — clicking the link in GitHub's UI tosample_report.html would only show source).

The demo shows the stats grid, trend sparkline, failure cards with embeddedscreenshots + step lists, and the collapsed Passed section.

Integrations

mcp-test-runner doesn't bundle third-party SDKs — it stays a puretest-execution + analysis layer. Real QA workflows are composed byrunning multiple MCP servers side-by-side in the same client config;Claude orchestrates the chain across servers. There's no MCP-to-MCPRPC — each server is independent, the AI client is the conductor.

The pairings below are the ones that complete the loop most often:

Honorable mention — Google Drive MCP: pairs with Google-Sheet-based TC management (read TCs from a sheet → generate_test → write status back).

Composing in your client config

All five run as separate processes alongside mcp-test-runner:

{
  "mcpServers": {
    "mcp-test-runner": { "command": "python", "args": ["-m", "mcp_test_runner.server"], "env": { "QA_RUNNER": "maestro" } },
    "atlassian":       { "command": "npx", "args": ["-y", "@atlassian/mcp"] },
    "slack":           { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-slack"] },
    "github":          { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"] }
  }
}

Then a single prompt walks the chain:

"Run the checkout suite. For each failure, open a JIRA in project QA with the RIDER format and the screenshot attached. Post the HTML report to #qa-bots when done."

Why this matters: mcp-test-runner stays focused on the test loop(analyze → generate → run → coach). JIRA / Slack / Sentry are entiredomains with their own dedicated servers — bolting them into this onewould dilute the scope, duplicate auth handling, and force every userto inherit dependencies they may not want.

本 repo 不打包任何第三方 SDK——維持「測試執行 + 分析」單一職責。實務上 QA 工作流是多個 MCP server 並存、由 Claude 編排跨 server 的 tool chain達成的。範例配套：JIRA / Slack / GitHub / Sentry / Filesystem 各自獨立 MCP server，配上 mcp-test-runner 拼出完整測試管線。

Publishing (maintainer-only)

Releases ship to PyPI via Trusted Publishing — no API tokens stored in the repo. The flow:

1. Bump version = "x.y.z" in pyproject.toml (via a normal PR — main is branch-protected).
2. After merge, tag main and push:

   git tag -a vX.Y.Z -m "vX.Y.Z — short summary"
   git push origin vX.Y.Z
   

3. Create a GitHub Release for that tag (gh release create vX.Y.Z ...).
4. The release event fires .github/workflows/publish.yml → builds sdist + wheel → uploads to PyPI.

One-time PyPI setup (must be done once before the first publish works):

• Sign in at https://pypi.org → enable 2FA.
• Project page → Settings → Publishing → add a pending publisher with:
  • Owner: kao273183
  • Repository: mcp-test-runner
  • Workflow filename: publish.yml
  • Environment name: pypi

After the first successful run, PyPI auto-promotes the pending publisher to a trusted one and subsequent releases authenticate via OIDC.

The workflow refuses to publish if the release tag doesn't match pyproject.version, which catches "tagged but forgot to bump" mistakes before they hit PyPI.

Support the project ☕

mcp-test-runner is built and maintained solo on nights and weekends. If it saved you time or shaped how your team thinks about AI-driven QA, a coffee keeps the late-night Maestro debugging sessions going:

Your support funds: keeping this repo free + actively maintained, more device variants for Maestro testing (real iPhones / Android tablets / BlueStacks), recorded tutorials for the QA community, and the next 2am bug hunt.

No ads, no sponsorships, no enterprise upsell — just the work.

Contributing

This repo is maintained solo. Ideas and bug reports are very welcome — please open an Issue or start a Discussion. I read every one and will implement what fits the project's direction.

External pull requests are auto-closed. Not because contributions aren't appreciated, but because keeping the codebase coherent under a single voice matters more here than the throughput a multi-contributor model would bring. If you really want a specific change, an Issue describing the problem gets you further than a PR.

本 repo 由我一人維護。歡迎透過 Issue / Discussion 提想法或回報問題，我會親自評估並實作。外部 PR 會自動關閉——不是不歡迎貢獻，而是想保持程式碼風格與走向一致。

License

MIT © 2026 Jack Kao — see LICENSE(中文翻譯參考: LICENSE.zh-TW.md; the English version isauthoritative).

In plain English: you can use this for anything (personal projects, commercialwork, modifications, redistribution). The only ask is that you keep thecopyright + license notice in any copy you ship. There's no warranty — useat your own risk.