Pcloudy adds conversational AI, database validation and MCP support to QPilot 2.0

Pcloudy on July 15 launched QPilot 2.0, a new version of its test automation agent that adds conversational AI for reporting, database validation across test flows and native MCP support for external AI tools. The update is designed to help teams test, validate and analyze across UI, API and database layers from one workflow.

Why it matters: - QPilot 2.0 pushes test automation closer to an end-to-end workflow, combining creation, execution, backend validation and analysis in one conversational interface. - The update is aimed at enterprises using AI-assisted testing pipelines that want fewer tool handoffs and faster verification across the full stack. - Native MCP support opens Pcloudy’s device cloud to external AI tools, including Claude and Cursor, which could make testing workflows easier to embed inside existing developer environments.

What happened: - Pcloudy announced QPilot 2.0 on July 15, 2026. - The release adds Ask AI, a conversational interface for test data and report analysis. - The release adds a Database Connector that brings backend validation into the same flow as UI and API testing. - The release adds native support for the Model Context Protocol, or MCP. - Pcloudy said the MCP integration connects Claude, Cursor and other MCP-compatible tools directly to its real device cloud.

The details: - Ask AI extends QPilot’s natural-language interface from test creation into reporting and analysis. - The Database Connector lets QPilot chain Web, API, Database and Mobile steps into one end-to-end test. - The Database Connector keeps context across each layer of the stack and produces a single unified report. - Typical use cases include checking whether a registration or checkout flow writes correctly to the database. - The Database Connector also supports validation of backend records immediately after a UI action, without switching to a separate query tool or writing custom scripts. - The Pcloudy MCP Server lets teams connect AI tools or IDEs directly to Pcloudy’s device cloud of 5,000+ real Android and iOS devices. - Through the MCP integration, AI agents can manage and execute test cases using natural language. - AI agents can also trigger manual or scripted tests on real Android and iOS devices. - AI agents can drive QPilot from within a chat or IDE session. - AI agents can retrieve reports, logs and device insights without leaving the development or chat environment. - Setup uses a Python-based MCP server called pcloudy-mcp and existing Pcloudy account credentials. - Pcloudy says the setup reduces context switching between an AI assistant, a device cloud and a reporting dashboard. - QPilot 2.0 is available now within Pcloudy. - Existing customers can access the new capabilities from the Pcloudy dashboard. - New users can request a demo via pcloudy.com.

Between the lines: - The launch reflects a broader move to make testing tools more conversational and more connected to the systems where engineers already work. - By adding database checks and MCP access, Pcloudy is positioning QPilot as more than a test runner; it is aiming to be a control layer for testing and analysis. - The company’s pitch is that AI testing becomes more useful when it can verify outcomes, not just simulate actions. - Pcloudy also appears to be targeting teams that want one workflow across UI, API, mobile and database validation instead of separate point tools.

What's next: - Pcloudy will likely use QPilot 2.0 to deepen adoption of its AI Agents Suite among enterprise testing teams. - The company is encouraging prospective customers to book a demo and evaluate the new features inside its platform. - Wider MCP adoption could determine how quickly tools like Claude and Cursor become part of day-to-day QA workflows.

The bottom line: - QPilot 2.0 turns Pcloudy’s test automation agent into a more conversational, cross-layer testing hub with direct hooks into external AI tools.