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Redefining Qa Multi Agent AI Automation Using MCP Protocol

by · github.com/sarthak1095/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

Unclaimed last commit 11 months ago ai
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48Fair

Scored 5 months ago · breakdown

About Redefining Qa Multi Agent AI Automation Using MCP Protocol

Redefining Qa Multi Agent AI Automation Using MCP Protocol is an MCP server published by sarthak1095 in the AI category: 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.

The repository has 16 stars and 13 forks, with the last commit 11 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.

Install

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

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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Security checks

Static checks derived from the repository 5 days ago. Heuristics on the source, not an audit.

  • ✓README presentThe repository ships documentation.
  • !License declaredNo license file. Legally you have no right to use it in a product.
  • !Tests presentNo tests found. Behaviour changes may go unnoticed between versions.
  • !Dependencies pinnedNo lockfile. Installs can pull different code over time.
  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

1 check has not been run yet.

README

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
  1. Navigate to the project directory:
cd Redefining-QA-Multi-Agent-AI-Automation-Using-MCP-Protocol
  1. Install dependencies:
pip install -r requirements.txt
  1. Configure config.json with MCP server endpoints and credentials.

Usage

  1. Run the multi-agent test workflow:
python run_tests.py
  1. View the consolidated test execution report in Markdown/HTML format.
  2. 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

README fetched from the repository 5 days ago. The original is authoritative.

Questions

About Redefining Qa Multi Agent AI Automation Using MCP Protocol

How do I install Redefining Qa Multi Agent AI Automation Using MCP Protocol?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Redefining Qa Multi Agent AI Automation Using MCP Protocol safe to use with an AI agent?

Its trust score is 48 out of 100 (fair). It passes 1 of the 5 static security checks run so far; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Redefining Qa Multi Agent AI Automation Using MCP Protocol still maintained?

The last commit was 11 months ago and 1 open issues. That's long enough that you should check whether the maintainer is responding to issues before depending on it.