原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Playwright Report MCP

作者:hubertgajewski版本 3.4.0i★ –i在你的电脑上运行
注册名称io.github.hubertgajewski/playwright-report-mcp

安装

概览

作者提供的说明(英文原文)

Run Playwright tests and surface structured results for AI agents doing test failure analysis.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 网页与浏览器

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: PW_ALLOWED_DIRS, PW_RESULTS_FILE

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "playwright-report": {
      "command": "npx",
      "args": [
        "-y",
        "playwright-report-mcp"
      ],
      "env": {
        "PW_ALLOWED_DIRS": "your-value",
        "PW_RESULTS_FILE": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add playwright-report --env PW_ALLOWED_DIRS=your-value --env PW_RESULTS_FILE=your-value -- npx -y playwright-report-mcp

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "playwright-report": {
      "command": "npx",
      "args": [
        "-y",
        "playwright-report-mcp"
      ],
      "env": {
        "PW_ALLOWED_DIRS": "your-value",
        "PW_RESULTS_FILE": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "playwright-report": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "playwright-report-mcp"
      ],
      "env": {
        "PW_ALLOWED_DIRS": "your-value",
        "PW_RESULTS_FILE": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • PW_ALLOWED_DIRS env
    path.delimiter-separated list of directories the per-call workingDirectory parameter may point at. Entries may be absolute or relative (resolved once against the server's launch cwd at startup). Defa…
  • PW_RESULTS_FILE env
    Absolute path override for the JSON reporter output file. Defaults to /test-results/results.json, computed per call.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 hubertgajewski 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“网页与浏览器”中的其他服务器比较i

  • 星标数多于此分类中其他 881 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 881 个服务器的 46% 更新
  • 基础检查结果等于或优于此分类中其他 881 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Playwright Report MCP 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(3.4.0)于43天前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它?

同一分类中有:Browser Use, Chrome DevTools MCP, MCP Server Browser

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Browser Use

作者:browser-use

Browser agents and stealth cloud browsers for AI: run web tasks, fill forms, extract data.

电脑或云端★ 120,087MIT17天前更新
安装

Chrome DevTools MCP

作者:ChromeDevTools

MCP server for Chrome DevTools

在你的电脑上运行★ 54,821Apache-2.016天前更新
安装

MCP Server Browser

作者:bytedance

MCP server for browser use access

在你的电脑上运行★ 39,550Apache-2.014个月前更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Playwright Report MCP

An MCP (Model Context Protocol) server for running Playwright tests and reading structured results, failed test details, and attachment content — designed for AI agents doing test failure analysis.


Table of contents

  • What it is
  • What it is NOT
  • Why
  • Quick start
  • Tools
  • Attachments
  • Installation
  • Configuration
  • Requirements
  • Troubleshooting
  • Development
  • Cutting a release
  • Contributing
  • Release
  • License

What it is

Playwright Report MCP gives an AI agent structured, token-efficient access to Playwright test outcomes. It runs your test suite, reads the JSON reporter output, and surfaces exactly what the agent needs: which tests failed, what the errors were, and the content of relevant attachments.

What it is NOT

There are many Playwright MCP servers that control a browser — they navigate pages, click elements, fill forms, and take screenshots. Playwright Report MCP is not one of those.

Browser automation MCPsPlaywright Report MCP
Examplesmicrosoft/playwright-mcp, executeautomation/mcp-playwrightthis project
PurposeLet an AI agent drive a browserLet an AI agent read test results
Runs testsNoYes
Returns pass/failNoYes
Surfaces error messagesNoYes
Reads attachment contentNoYes

Why

The problem with existing approaches

Default reporters (`list` / `dot`) — Playwright's default reporters print human-readable output to stdout. Compact, but lossy: no attachment paths, no retry breakdown, no structured data.

HTML reporter (report.html) — A self-contained SPA bundle (typically 2–50 MB). Not machine-readable as text and exceeds any LLM context window.

Reading `results.json` directly — Works, but a full JSON report for even a small test suite is 10,000–20,000 tokens. For a failing test, most of that is passing test metadata you don't need.

What Playwright Report MCP does instead
  • Filters results.json to only failed tests
  • Returns structured, typed JSON the agent can act on immediately
  • Exposes individual attachments by name so the agent fetches only what it needs
  • Works on results produced by anyone — CI pipeline, a human, or the agent itself
Token cost comparison (one failed test in a 20-test suite)

Approximate input token counts based on Claude tokenization (~3–4 characters per token for mixed JSON/text content).

What you needWithout MCP — approachTokens (no MCP)With MCP — tool callsTokens (MCP)Savings
Error message only — live runnpx playwright test, read stdout (list/dot)~500–1,200run_tests + get_failed_tests~300–500~2×
Error message only — existing resultsRead full results.json~12,500–23,000get_failed_tests~300–500~25–45×
+ page state at failure+ read error-context file~15,000–26,000+ get_test_attachment('error-context')~2,800–3,500~4–7×
+ custom text attachments¹+ read attachment files~16,200–28,500+ get_test_attachment ×2~3,300–5,500~4–5×
+ full page HTML snapshot²+ read snapshot file~41,000–103,000+ get_test_attachment~33,300–85,500~1.2×

¹ Custom text attachments — e.g. AI diagnosis (~500–2,000 tokens) and console logs (~200–500 tokens) added via testInfo.attach() in your own fixtures.

² Full page HTML snapshot — a custom fixture that attaches the full rendered page HTML on failure. Large pages alone can reach 30,000–80,000 tokens and dominate cost regardless of whether MCP is used.

Key observations:

  • For a live run, stdout (list/dot) is compact but gives the agent no path to attachment content — dead end for deeper analysis
  • Reading results.json directly costs ~12,500–23,000 tokens even when only one test failed — most of it is passing test metadata the agent doesn't need
  • The biggest MCP gains are in the middle rows: getting error messages + page state from existing results at ~4–45× lower token cost
  • Full page HTML snapshot dominates cost either way; skipping it in favour of error-context is the single largest optimisation available
CI failure analysis

The primary use case: your CI pipeline runs the tests, the agent picks up the results after the fact and diagnoses failures. get_failed_tests reads results.json regardless of who triggered the run. No re-run needed.


Quick start

1. Install via npx (recommended)

No clone or build step needed — npx downloads and runs the server automatically:

{

"mcpServers": { "playwright-report-mcp": { "command": "npx", "args": ["-y", "playwright-report-mcp"], "type": "stdio" } } }

Or build from source:

git clone https://github.com/hubertgajewski/playwright-report-mcp.git

cd playwright-report-mcp npm install && npm run build

2. Add the JSON reporter to your Playwright project

// playwright.config.ts

reporter: [ ['json', { outputFile: 'test-results/results.json' }], ['html'], // keep any existing reporters ],

3. Register in `.mcp.json`

{

"mcpServers": { "playwright-report-mcp": { "command": "npx", "args": ["-y", "playwright-report-mcp"], "type": "stdio" } } }

4. Ask your AI agent

Run the Playwright tests and tell me what failed.


Compatibility

Tested with Claude Code (CLI). Should work with any MCP-compatible client that supports stdio transport, including Claude Desktop, Cursor, Cline, Windsurf, and Continue.dev — but these have not been verified.

The stdio server supports both MCP protocol eras from one entrypoint:

  • Modern: protocol revision 2026-07-28, selected by clients using version negotiation (for example, versionNegotiation: { mode: "auto" }).
  • Legacy: supported 2025-era revisions, selected by clients that use the traditional initialize handshake. This remains the default behavior in the MCP client SDK.

The opening exchange pins one era for the connection lifetime. A client that pins an unsupported revision receives an explicit negotiation error; the server does not silently switch it to another era.


Tools

The project-scoped tools accept an optional workingDirectory parameter — see Multi-worktree support. get_run_status can use either a runId from run_tests with wait: false, or a workingDirectory lookup for the latest tracked run.

run_tests

Runs the Playwright test suite and returns structured pass/fail results.

InputTypeDescription
workingDirectorystring (optional)Playwright project directory. Absolute or relative to the MCP server launch directory. Defaults to ".". Must be under PW_ALLOWED_DIRS — see Multi-worktree support.
specstring (optional)Spec file path relative to the project directory, e.g. tests/login.spec.ts. Must stay within the project directory.
browserenum (optional)Chromium, Firefox, Webkit, Mobile Chrome, Mobile Safari
tagstring (optional)Tag filter, e.g. @smoke
timeoutinteger (optional)Timeout in milliseconds for the whole test run. Defaults to 300000 (5 min). Use a larger value for long suites or a smaller one to fail fast. When the run is killed by this timeout, the tool returns an explicit error rather than a generic non-zero exit.
waitboolean (optional)Wait for completion before returning. Defaults to true. Set to false to start a background run and poll it with get_run_status.
updateSnapshotsenum (optional)Update snapshot baselines. One of all, changed, missing, none. Playwright's default is missing; changed updates differing + missing. Omit to leave existing baselines alone.
headedboolean (optional)Run with a visible browser window. Omitting or setting false leaves playwright.config.ts intact — Playwright has no --no-headed flag, so false does not force headless when the config sets headed.
workersinteger (optional)Number of parallel workers. Positive integer only; the "50%" string form is not yet supported.
retriesinteger (optional)Maximum retry count for flaky tests. 0 explicitly disables retries; omit to use the project's config.

| maxFailures | integer (optional) | Stop the run after this many failures. Positive integer.

只显示了开头部分;其余内容请在仓库中阅读

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