A practical Browser MCP Agent series for testing the ToolShop application using Python, FastMCP, Playwright, GitHub Models API, and professional HTML reporting.
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.
- Manual Testers
- QA Engineers
- Automation Testers
- SDETs
- QA Leads
- QA Architects
- Students learning MCP
- Anyone learning Agentic AI for Software Testing
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
| 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 |
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/
Install:
- Python 3.12+
- Git
- VS Code
- Chromium via Playwright
- GitHub Models access for V5/V6
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 chromiumCreate .env in the project root only if you want V5/V6 to use GitHub Models:
GITHUB_TOKEN=your_github_models_token_hereV1 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 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.pyEach 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/
Run:
python .\v1_browser_control\main.pyMenu:
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
Run:
python .\v2_search_testing_agent\main.pyInputs:
Existing product: hammer
Invalid product: xyznotfound
Report name: search_test_report.html
Output:
v2_search_testing_agent/reports/search_test_report.html
Run:
python .\v3_cart_testing_agent\main.pyInputs:
Product: hammer
Report name: cart_test_report.html
Output:
v3_cart_testing_agent/reports/cart_test_report.html
Run:
python .\v4_checkout_testing_agent\main.pyInputs:
Product: saw
Report name: checkout_test_report.html
Output:
v4_checkout_testing_agent/reports/checkout_test_report.html
Run:
python .\v5_ai_exploratory_testing_agent\main.pyExample 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
Run:
python .\v6_autonomous_browser_qa_agent\main.pyExample 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
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()
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
Check status:
git statusRecommended .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 mainIf your branch is master:
git push origin masterYou 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
- 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
Neelam Pal
QA Architect | AI in Testing | MCP | Agentic AI | Quality Engineering
MIT License.