Redefining-QA-Multi-Agent-AI-Automation-Using-MCP-Protocol

This project demonstrates how to build a multi-agent AI automation framework using the Model Context Protocol (MCP). The setup enables LLMs (Claude AI in this case) to autonomously execute UI flows, API validations, file operations, and cross-system authentication workflows through standardized tool interfaces

Key Features

• Multi-Agent System:

  • Web Automation Agent – Automates browser workflows using Playwright via MCP servers.
  • API & File System Agent – Executes API tests and interacts with local data.
  • Excel Agent – Reads and writes Excel files for scenario-driven data management.

• End-to-End Test Scenarios:

  1. Registration Validation Workflow – Ensures proper validation for empty/invalid registration.
  2. Full Registration + Authentication (UI & API) – Validates dual authentication flows and persists credentials.
  3. Password Recovery & Cross-Channel Authentication – Automates password reset and re-authentication via UI and API.

• Comprehensive Reporting:

  • Test summary dashboards
  • Detailed execution reports
  • Test artifacts
  • Test coverage analysis and matrix
  • Key observations

• Agentic AI Orchestration:

  • Coordinated multi-agent workflow using Claude AI as the client
  • Intelligent prompt-driven execution
  • Seamless synchronization across UI, API, and file systems

Getting Started

Prerequisites

• Python 3.10+
• Node.js (for Playwright)
• Claude AI client access
• MCP servers configured

Installation

1. Clone the repository:

git clone https://github.com/sarthak1095/Redefining-QA-Multi-Agent-AI-Automation-Using-MCP-Protocol.git

2. Navigate to the project directory:

cd Redefining-QA-Multi-Agent-AI-Automation-Using-MCP-Protocol

3. Install dependencies:

pip install -r requirements.txt

4. Configure config.json with MCP server endpoints and credentials.

Usage

1. Run the multi-agent test workflow:

python run_tests.py

2. View the consolidated test execution report in Markdown/HTML format.
3. Check Excel outputs under newdata.xlsx for test data results.

Test Scenarios & Outcomes

1. Empty Registration Validation: All field-level validations triggered successfully.
2. Full Registration + Authentication: UI and API login flows validated, credentials persisted to Excel.
3. Password Recovery & Reauthentication: End-to-end password reset validated across UI and API, logout verified.

✅ Overall Status: All scenarios passed (100% success rate)

Contributing

Contributions are welcome! Please create an issue or pull request for bug fixes, improvements, or new test scenarios.

References

• Learn Agentic AI – Build Multi-Agent Automation Workflows by Rahul Shetty
• MCP Protocol Documentation – Anthropic