Pcloudy adds Model Context Protocol (MCP), enabling devs to trigger tests on real devices using natural language in their IDEs.
Pcloudy , a leading digital experience testing platform, announced native support for Model Context Protocol (MCP), enabling developers and QA engineers to interact with Pcloudy ’s testing infrastructure directly from their IDEs. This integration brings intelligent testing assistance into the natural flow of coding and debugging, similar to how developers use tools like Cursor or GitHub Copilot.
“Now, any developer can ask their AI assistant in their IDE to run tests on specific devices, analyze failure patterns, or generate test cases—all without leaving their coding environment. It’s like having a senior QA engineer embedded in your development workflow.”
With IDE integration through MCP, developers can perform complex testing tasks using natural language. A developer debugging an issue can simply ask, “Run this test on all Samsung devices with Android 14” or “What caused this test to fail on iOS but pass on Android?” The AI agent instantly accesses Pcloudy ‘s infrastructure to execute commands and return insights.
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The deep platform access enabled by MCP gives AI agents full control across the testing lifecycle. They can leverage Pcloudy ‘s real device cloud of 5,000+ devices, use Qpilot for AI-driven test creation, orchestrate test execution through Quantum Run, and analyze results. This seamless integration eliminates context switching and dramatically accelerates the development cycle.
“What excites me most is how MCP democratizes access to our entire testing ecosystem,” explained Tiwari. “Whether you’re using Claude in VS Code, ChatGPT in your terminal, or a custom AI assistant, you now have full access to Pcloudy ‘s capabilities. Complex operations that previously required navigating multiple interfaces can now be performed with simple commands.”
The integration becomes particularly powerful when combined with Pcloudy ‘s AI-driven tools. Developers can ask their AI assistant to “Create tests for the new payment module using Qpilot, run them on high-priority devices, and summarize any failures.” The MCP-enabled agent orchestrates this entire workflow, from test generation to execution to analysis, returning results in seconds rather than hours.
For enterprises, Pcloudy ‘s MCP implementation includes robust security features with OAuth 2.1 authentication, role-based access control, and complete audit trails. Organizations can deploy MCP servers on-premise or in private clouds, ensuring testing data remains within controlled environments.
Development teams are already discovering innovative applications. Some use MCP to create automated testing workflows triggered by code commits, while others build custom AI agents that understand their specific testing patterns and automatically suggest relevant test cases based on code changes.
Pcloudy provides comprehensive resources for developers including pre-built MCP configurations, SDKs in Python, TypeScript, and Java, and detailed documentation.
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