In a world where AI is rapidly transforming how we write code, design systems, and deliver products, one crucial part of the software development lifecycle has remained relativelydisconnected from this revolution: testing. While AI tools can suggest test cases, generate automation scripts, or point out risky code changes, they have had limited direct access to the actual testing infrastructure that validates those ideas — until now.

With native support for the Model Context Protocol (MCP), Pcloudy is changing that paradigm.

Pcloudy has become one of the first digital experience testing platforms to support MCP Servers, a move that positions it at the center of what we’re calling AI-native testing — a new way of working where AI agents are not just assistants but active participants in the testing lifecycle.

What is MCP, and Why Should You Care?

Model Context Protocol (MCP) is a standardized way for AI applications to interact with external systems via structured, machine-readable JSON contexts. Think of it as an API purpose-built for AI. Where traditional APIs cater to human-written code, MCP is designed for AI Agents that reason in context. These agents don’t just “call” endpoints; they interpret, act, and respond to evolving conditions. By supporting MCP Servers, Pcloudy effectively transforms its entire testing infrastructure into an AI-accessible knowledge base.

Think of MCP as a universal translator between AI tools and data platforms. It allows AI agents to interact with structured external data sources – like testing platforms, observability tools, analytics dashboards, and more. The MCP Server is able to do this through lightweight JSON-based configurations.

In practical terms, it means your AI assistant can now “understand” your testing environment the same way it understands a user prompt or a code snippet.

Pcloudy’s implementation of MCP turns its massive infrastructure of 5,000+ real devices and browser combination into a machine-readable, queryable knowledge base. Your AI agents can plug in directly, ask complex questions, take action, and make data-driven decisions like a senior QA engineer, only faster and at scale. What’s more is that the deep test data, logs, and performance insights are so seamlessly shared for various testing functionalities.

From Manual Analysis to Autonomous Understanding

Traditionally, QA teams spend countless hours combing through logs, reviewing failed test cases, and trying to correlate flaky behaviors across devices and environments. Even with automation, the insights were siloed, difficult to interpret at scale, and slow to turn into action.

With Pcloudy’s MCP Server integration, this entire process changes.

AI agents can now:

• Instantly access real-time and historical test results

• Query device metrics across thousands of Android and iOS environments

• Analyze crash logs, performance bottlenecks, and flaky tests

• Make data-driven recommendations — or take action autonomously

Imagine asking your AI assistant: “Which devices failed the checkout test last night due to memory issues?” and getting an accurate, contextualized answer in seconds — drawn from thousands of test executions across your entire infrastructure.

That’s not just a productivity boost. That’s a fundamental shift in how software teams operate.

How Can MCP Servers Help?

The potential applications of MCP servers inside Pcloudy are wide-ranging and revolutionary:

Debugging Faster, Smarter

Previously, debugging a test failure meant sifting through logs, screenshots, and device metrics — often across dozens of test runs. With MCP, AI agents can access this context in milliseconds and correlate failure patterns across releases, devices, or environments. You can now ask your AI assistant:

“What memory-related issues were observed in the last release on Samsung devices?” And get an answer instantly — no queries to write, no logs to parse.

Running Tests Through AI Tools

With MCP, your AI agent doesn’t just write test cases — it can execute them. Whether you’re using Claude, ChatGPT, or a custom co-pilot, your tool can now trigger tests directly on Pcloudy’s infrastructure. From mobile regression suites to device compatibility checks, testing becomes a part of the AI-driven dev workflow, not an isolated stage. 

Managing & Orchestrating Tests Autonomously

AI agents can now manage test runs dynamically: prioritizing critical test cases, skipping redundant ones, or launching exploratory tests when anomalies are detected. Combined with Pcloudy’s Quantum Run, this creates a closed feedback loop where testing improves continuously based on past results and future risks.

Making Test Data Truly Actionable

Imagine querying test history as easily as you’d ask a teammate:

“Which tests are flaky and frequently fail on iOS 17?” “Show me all login-related crashes in the last 10 days.” MCP allows agents to ask these questions and act on them — running additional tests, flagging bugs, or notifying the right stakeholders — all in real time.

Why This Is a Breakthrough for Testing Teams

For years, testing has been manual, fragmented, and reactive. Even the most advanced test automation setups still rely on human intervention for analysis, prioritization, and orchestration.

With MCP, we enter the world of AI-native testing — where intelligent agents can fully participate in the testing lifecycle.

Pcloudy’s infrastructure becomes not just a test bed, but a dynamic environment where agents can learn, adapt, and optimize in real time. 

This means:

• Fewer delays between development and feedback

• Smarter test coverage based on real usage and risk

• Higher quality releases at greater speed and scale

Most importantly, it allows QA teams to focus on strategy and innovation, while agents handle the execution, analysis, and reporting.

Secure, Scalable, and Built for the Enterprise

This isn’t just cutting-edge tech — it’s enterprise-grade from day one.

Pcloudy’s MCP implementation includes:

• OAuth 2.1 for secure authentication

• Role-based access controls for fine-grained permissions

• On-prem and private cloud support for data residency and compliance

• Audit logs for every action an AI agent performs

Organizations can safely deploy AI-driven testing workflows without compromising governance or control.

We’re Just Getting Started

At Pcloudy, we see MCP not as a protocol — but as an enabler of the next phase of intelligent software delivery. The same way cloud-native changed how we host apps, AI-native is changing how we validate them. 

Some of the world’s most forward-thinking teams are already:

• Building custom co-pilots that understand their unique test architectures

• Creating multi-agent workflows for test triage and remediation

• Using AI to predict failures before they happen

And this is only the beginning.

Get Started with MCP on PcloudyE xplore setup details at: 🔗 pcloudy.com/docs/mcp-server-configuration-with-pcloudy

The future of testing is intelligent. It’s automated. It’s AI-native.

And with MCP servers on Pcloudy — it’s already here.