"Release with Confidence, Not Hope."
AI that doesn't just assist — it acts. The Digital.ai MCP server connects your AI agent directly to your testing platform, turning natural language into real platform actions across administration, analysis, and test execution.
The MCP server gives your AI agent the context it needs to act with precision: best practices, API knowledge, data formats, and device protection rules — all built in.
Stop navigating dashboards. Start asking questions. Manage your entire testing environment — devices, projects, tags, regions, users — through natural language.
Your tests generate enormous amounts of data. AI is built to parse it. Turn logs, failure reports, and utilization data into clear, actionable intelligence — in minutes, not days.
Shift left without shifting burden. Developers write mobile and web tests in natural language from their IDE — no boilerplate, no framework expertise required. Real devices, real browsers, real results.
These are real prompts. Real outputs. No scripts, no pipelines — just a conversation with your platform.
An overview of the platform value and all three use cases — administration, analysis, and test generation. For deeper, use-case-focused walkthroughs, see the feature series below.
Available on GitHub and installed in minutes via Docker. No new tools, no new workflows — it plugs into the AI agent you're already using.
Available on GitHub. Compatible with Claude Desktop, Claude Code (VS Code), Claude Code (JetBrains / Android Studio), GitHub Copilot (VS Code), Cursor, and any MCP-compatible AI agent.
Point the server at your Digital.ai environment. It automatically scopes to your projects, devices, and data.
Ask anything. Administer, analyze, generate. The MCP server handles the API calls, context, and best practices — you own the outcome.
Every action is visible and confirmable. Human-in-the-loop is a design principle, not an afterthought.
The Digital.ai MCP server is available now on our Community page. Documentation, example prompts, and everything you need to get started are waiting for you.
Start Your Journey NowNot a demo. Not a slide review. A working session — you leave with a live MCP connection, a generated test script against a real device, and a team rollout plan.
Request a WorkshopContact your Digital.ai account representative or use the link above to request a session.
Want to build the business case first? Read the economics brief →
MCP server live and connected to your device farm — running natural language queries against your real environment before the session ends.
A complete Appium test script generated from a live device inspection session against your app — element IDs sourced from reality, not invented.
Documented next steps tailored to your team — prerequisites, access model, and an expansion plan you can bring back to your organization.
The server is model-agnostic and works with any MCP-compatible agent. Tested with Claude Desktop, Claude Code (VS Code), Claude Code (JetBrains / Android Studio), GitHub Copilot (VS Code), and Cursor. If your agent supports the Model Context Protocol, it will work.
No. Connecting an AI to a raw API gives it access to endpoints — nothing more. Without the MCP, the AI has to research what connects to what, figure out available endpoints, and work through the API structure from scratch on every session. That overhead costs tokens and produces mistakes. The Digital.ai MCP server provides the context layer — best practices, data formats, API rules, boilerplates, and device protection guardrails — so the agent behaves like someone who's used the platform for years. It's the difference between handing someone a key and handing them a map. And it's actually more token-efficient than going direct.
Yes, with appropriate review. All actions are visible and confirmable before execution. The server requires affirmative confirmation before any records are deleted, and we strongly recommend keeping human review in any workflow that performs writes or bulk changes.
No. That's the point. The MCP server selects the right language, delivers the right boilerplate, and generates a best-practice test from a plain English description. Developers can contribute tests without leaving their IDE or learning a new framework.
Test generation still works with compiled apps. The process is iterative rather than source-driven, but it remains dramatically faster than manually hunting for XPath identifiers. QA teams can also use the Test Manager in the platform UI to build and manage test suites.
The MCP server is available on GitHub. Visit digitalai-opensource/digital-ai-testing-mcp for documentation, installation instructions, and a library of example prompts to get you up and running immediately.
It actually reduces it. Without the MCP, an AI agent working against a raw API has to research available endpoints, figure out data formats, and work through API structure from scratch — that exploratory overhead consumes tokens and produces mistakes along the way. The MCP's pre-built context layer eliminates that entirely: the agent arrives already knowing best practices, API rules, and data formats, so it reaches the answer faster and with fewer corrective cycles. The practical framing: apply agentic testing where it creates real leverage — high-volume analysis, complex multi-device queries, tasks that would take a human days of manual work. A 156-device utilization report in 3 minutes is a very different ROI calculation than using an agent to run a single test you could click yourself. For a detailed breakdown of build-vs.-buy economics and where the real cost is hiding, see The Hidden Cost of Bringing AI to Your Testing Stack.