QA Browser MCP Agent

QA Browser MCP Agent

An MCP server that enables browser-based QA testing of web applications using Playwright, with support for manual and AI-driven test execution and HTML reporting.

Category
访问服务器

README

QA Browser MCP Agent for ToolShop

A practical Browser MCP Agent series for testing the ToolShop application using Python, FastMCP, Playwright, GitHub Models API, and professional HTML reporting.

Python FastMCP Playwright MCP License

Overview

This repository demonstrates how to build a QA Browser MCP Agent for ToolShop:

https://practicesoftwaretesting.com

The project evolves from basic browser control to autonomous AI-powered QA execution.

Who This Is For

  • Manual Testers
  • QA Engineers
  • Automation Testers
  • SDETs
  • QA Leads
  • QA Architects
  • Students learning MCP
  • Anyone learning Agentic AI for Software Testing

Architecture

User / QA Engineer
        ↓
Python Main Program
        ↓
FastMCP Client
        ↓
STDIO Transport
        ↓
FastMCP Browser Server
        ↓
Playwright
        ↓
Chrome Browser
        ↓
ToolShop Website
        ↓
Screenshots + HTML Reports

For AI versions:

User Goal
        ↓
GitHub Models API
        ↓
AI Test Plan / Exploratory Test Ideas
        ↓
Browser MCP Server
        ↓
Playwright Execution
        ↓
Professional QA Report

Versions

Version Folder Purpose AI Used HTML Report
V1 v1_browser_control Browser control basics No No
V2 v2_search_testing_agent Search functionality testing No Yes
V3 v3_cart_testing_agent Shopping cart testing No Yes
V4 v4_checkout_testing_agent Checkout readiness testing No Yes
V5 v5_ai_exploratory_testing_agent AI exploratory testing Yes Yes
V6 v6_autonomous_browser_qa_agent Autonomous browser QA Yes Yes

Project Structure

QA_Browser_MCP_Agent/
├── README.md
├── requirements.txt
├── .gitignore
├── LICENSE
├── CHANGELOG.md
├── CONTRIBUTING.md
├── docs/
│   ├── architecture.md
│   ├── version-comparison.md
│   ├── troubleshooting.md
│   └── sample-goals.md
├── v1_browser_control/
├── v2_search_testing_agent/
├── v3_cart_testing_agent/
├── v4_checkout_testing_agent/
├── v5_ai_exploratory_testing_agent/
└── v6_autonomous_browser_qa_agent/

Prerequisites

Install:

  • Python 3.12+
  • Git
  • VS Code
  • Chromium via Playwright
  • GitHub Models access for V5/V6

Setup

cd C:\GIT\qa_mcp_series
git clone https://github.com/YOUR_USERNAME/QA_Browser_MCP_Agent.git
cd QA_Browser_MCP_Agent

python -m venv .venv
.venv\Scripts\activate

pip install -r requirements.txt
playwright install chromium

Environment Variables

Create .env in the project root only if you want V5/V6 to use GitHub Models:

GITHUB_TOKEN=your_github_models_token_here

V1 to V4 do not need this token.

V5 and V6 also include fallback logic, so they can still run if the token is missing or quota is exhausted.

Run Commands

Run all commands from project root.

python .\v1_browser_control\main.py
python .\v2_search_testing_agent\main.py
python .\v3_cart_testing_agent\main.py
python .\v4_checkout_testing_agent\main.py
python .\v5_ai_exploratory_testing_agent\main.py
python .\v6_autonomous_browser_qa_agent\main.py

Output Locations

Each version stores its own evidence:

v2_search_testing_agent/screenshots/
v2_search_testing_agent/reports/

v3_cart_testing_agent/screenshots/
v3_cart_testing_agent/reports/

v4_checkout_testing_agent/screenshots/
v4_checkout_testing_agent/reports/

v5_ai_exploratory_testing_agent/screenshots/
v5_ai_exploratory_testing_agent/reports/

v6_autonomous_browser_qa_agent/screenshots/
v6_autonomous_browser_qa_agent/reports/

Version 1: Browser Control MCP

Run:

python .\v1_browser_control\main.py

Menu:

1. Show Available MCP Tools
2. Open ToolShop and Capture Screenshot
3. Get ToolShop Homepage Info
4. Verify ToolShop Homepage Loads
0. Exit

Expected output:

v1_browser_control/screenshots/toolshop_homepage.png

Version 2: Search Testing Agent

Run:

python .\v2_search_testing_agent\main.py

Inputs:

Existing product: hammer
Invalid product: xyznotfound
Report name: search_test_report.html

Output:

v2_search_testing_agent/reports/search_test_report.html

Version 3: Cart Testing Agent

Run:

python .\v3_cart_testing_agent\main.py

Inputs:

Product: hammer
Report name: cart_test_report.html

Output:

v3_cart_testing_agent/reports/cart_test_report.html

Version 4: Checkout Testing Agent

Run:

python .\v4_checkout_testing_agent\main.py

Inputs:

Product: saw
Report name: checkout_test_report.html

Output:

v4_checkout_testing_agent/reports/checkout_test_report.html

Version 5: AI Exploratory Testing Agent

Run:

python .\v5_ai_exploratory_testing_agent\main.py

Example goals:

Perform security-oriented exploratory testing on ToolShop search
Explore ToolShop search from a usability perspective
Perform boundary testing on ToolShop search
Test ToolShop search functionality like a functional QA engineer

Output:

v5_ai_exploratory_testing_agent/reports/ai_exploratory_search_report.html

Version 6: Autonomous Browser QA Agent

Run:

python .\v6_autonomous_browser_qa_agent\main.py

Example goals:

Test ToolShop search functionality like a senior QA engineer
Perform security-focused browser QA testing on ToolShop search
Perform usability-focused testing on ToolShop product search
Perform boundary and negative testing on ToolShop search

Output:

v6_autonomous_browser_qa_agent/reports/autonomous_browser_qa_report.html

Important Concept: Why Single-Flow Tools Are Used

In MCP STDIO mode, each tool call may start a new server process. Browser state may not persist across separate menu options.

That is why this project uses stable single-flow tools:

open_toolshop_and_capture_screenshot()
run_search_functionality_tests()
run_cart_functionality_tests()
run_checkout_functionality_tests()
run_ai_exploratory_search_tests()
run_autonomous_browser_qa()

Troubleshooting Summary

Detailed troubleshooting is available in:

docs/troubleshooting.md

Common issues covered:

  • Browser state not persisting
  • Unknown tool
  • MCP connection closed
  • Missing GitHub token
  • Same AI results for every goal
  • GitHub Models rate limit
  • Playwright timeout
  • Checkout button not found
  • Screenshot links not opening
  • Browser executable missing

Git Commands

Check status:

git status

Recommended .gitignore should exclude:

.env
.venv/
__pycache__/
screenshots/
reports/
*.html
*.png

Commit:

git add README.md requirements.txt .gitignore LICENSE CHANGELOG.md CONTRIBUTING.md docs/ v1_browser_control/ v2_search_testing_agent/ v3_cart_testing_agent/ v4_checkout_testing_agent/ v5_ai_exploratory_testing_agent/ v6_autonomous_browser_qa_agent/
git commit -m "Add QA Browser MCP Agent with six progressive versions"
git push origin main

If your branch is master:

git push origin master

Learning Outcomes

You will learn:

  • MCP client-server architecture
  • Browser automation using Playwright
  • How to expose browser actions as MCP tools
  • Why STDIO MCP can be stateless
  • How to design stable browser-agent workflows
  • How to generate professional HTML reports
  • How AI can generate exploratory test ideas
  • How to evolve from automation scripts to autonomous QA agents

Future Enhancements

  • Excel reporting
  • PDF reporting
  • Console log capture
  • Network log capture
  • Accessibility checks
  • Visual validation
  • Login flow testing
  • Checkout form validation
  • Bug report generation
  • AI root cause analysis
  • GitHub Actions
  • Docker support

Author

Neelam Pal
QA Architect | AI in Testing | MCP | Agentic AI | Quality Engineering

License

MIT License.

推荐服务器

Baidu Map

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。

官方
精选
JavaScript
Playwright MCP Server

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。

官方
精选
TypeScript
Magic Component Platform (MCP)

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。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

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。

官方
精选
本地
TypeScript
VeyraX

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。

官方
精选
本地
graphlit-mcp-server

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。

官方
精选
TypeScript
Kagi MCP Server

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。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

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。

官方
精选