AI is transforming software testing—but without clear context, even the smartest models can fall short. The new Model Context Protocol (MCP) aims to solve that problem, and it’s picking up momentum fast. Here’s what QA and development teams need to know—and why it matters right now. If you have questions about how we’re building for the future or how this fits into your testing strategy, let us know—we’d love to talk.
What Is MCP?
MCP, or Model Context Protocol, is an open standard designed to help applications provide AI models with structured context. Think of it as a standardized way for tools and systems to tell an AI assistant what’s going on—who the user is, what they’re doing, and what resources are available.
Anthropic introduced MCP in late 2024, and it’s already being adopted by major players like OpenAI, Microsoft, and testing leaders building next-generation AI workflows. Addy Osmani, an engineering leader at Google, calls MCP “the USB-C of AI integrations,” highlighting its potential to standardize the connection between tools and intelligent agents.
Why Context Matters in AI-Assisted Testing
Large language models (LLMs) are only as good as the context they receive. Without proper inputs, you get generic outputs—or worse, hallucinations. For QA teams using AI to generate tests, interpret failures, or automate user flows, missing context leads to fragile results and wasted time.
MCP helps solve this by passing structured information to the model: which test framework is in use, what files are open, what code just changed, and more. That means faster, more relevant AI assistance—and more accurate automation.
What MCP Enables in Testing Workflows
MCP makes it easier for tools and AI assistants to share structured context—like which framework is active, what code changed, or what the user is trying to do. That unlocks more accurate test generation, better debugging, and scalable, reusable automation.
It also supports dynamic discovery, so AI systems can find and connect with available tools at runtime—no brittle configs or manual setup required.
As testers ourselves, we take a measured approach to adopting new AI standards like MCP. That means vetting integrations for stability and reliability, so our customers can move fast without sacrificing trust.
Why It’s a Big Deal Now
There are two key reasons to pay attention to MCP today:
First, the standard is taking off. Thought leaders like Angie Jones, Filip Hric, Tariq King, and Addy Osmani are publishing real-world MCP demos and contributing open-source tools. It’s not theoretical anymore—it’s happening.
Second, the stakes are high. As more testing platforms integrate AI (including Applitools Autonomous), the ability to connect tools through open standards like MCP is becoming a competitive differentiator.
How Applitools Fits In
Applitools has long focused on intelligent automation—delivering AI-powered test creation, visual validation, and self-healing across platforms. As open standards like MCP emerge, we’re building on that foundation to extend context-sharing across tools, so teams can:
- Automatically create or update visual and functional tests based on code changes
- Route test context through the pipeline for faster root cause analysis
- Improve AI-generated tests with better accuracy and explainability
Security is also critical. As MCP evolves, host-mediated permissions and encrypted communication protocols are being considered by contributors to ensure context is shared safely and responsibly.
At Applitools, we’re building these principles directly into the future of Autonomous and Eyes—and we’d love to walk you through what’s on our roadmap. If you’re already an Applitools customer, reach out to your account team to schedule a preview conversation. If you’re not already using Applitools, schedule time with one of our testing specialists—we’re here to help.
Quick Answers
MCP is an open standard introduced by Anthropic in late 2024. It defines a structured way for applications to provide AI models with context—such as user intent, file state, or tool availability—so that the model can respond more accurately and usefully.
Without the right context, even powerful AI models can produce generic or fragile outputs. MCP helps solve this by enabling structured, dynamic context sharing between testing tools and AI assistants. That makes test automation more precise, reusable, and pipeline-aware.
Unlike custom or one-off integrations, MCP is designed to be open and interoperable—think of it as the “USB-C” for connecting AI to software tools. It emphasizes flexibility, dynamic discovery, and standardized communication between tools and intelligent agents.
