AI Testing in 2025: Why the Future of Quality Depends on the Model Context Protocol (MCP)
By Eduard Dubilyer, CTO & Automation Testing Expert at Skipper Soft
1. Life in the Prompt Chaos
Let’s start with some honesty. If you work in QA, test automation, or build testing tools today, you’ve probably tried using ChatGPT, Copilot, or other AI assistants.
You copy requirements from Jira, paste them into a prompt, refine the context, and get beautiful test cases… that know nothing about your product, data, or system version. Then you start over.
Each time you paste a log snippet or test case into an AI chat, it feels like you’re carrying buckets of water to a place that should already have plumbing.
And that’s the core pain of the AI era in testing: AI is smart, but it has no context.
2. MCP Appears — A Universal Language for AI and Tools
The Model Context Protocol (MCP) isn’t just another trendy acronym. It’s a new standard for how AI communicates with external systems — including testing tools, CI/CD pipelines, TMS, Jira, and everything else you use daily.
If before we had to manually “feed” AI with data and context, MCP creates a single interface through which AI can securely access tools, data, and even execute actions.
Think of it as giving your AI a USB port to plug into your testing environment.
3. What Is the Model Context Protocol (MCP)?
Simply put, MCP is a protocol for interaction between language models and external systems. It defines:
- How AI discovers available tools.
- How does it gain secure access to them?
- How it preserves context and state.
- How it exchanges structured data.
Technically, MCP acts as an API gateway, connecting models (such as GPT or Claude) to your Jenkins, Qase, Jira, or test automation frameworks.
MCP turns an “intelligent chat” into an integrated team member that understands your project and can act within it.
4. Why MCP Matters for QA and Automation
For QA Engineers:
- No more manual context transfer — AI can access TMS, Jira, and test results directly.
- Automatically generate reports, summarize defects, and analyze quality trends.
- Receive AI recommendations based on your project’s actual data.
For Automation Engineers:
- AI can trigger, monitor, and analyze test runs.
- Test generation becomes contextual — the AI knows your framework, structure, and environment.
- Enable self-healing automation, where AI can fix failed tests autonomously.
For Companies:
- MCP establishes a unified standard for AI integration into QA and DevOps ecosystems.
- Reduces risk, increases reproducibility, and makes AI-driven processes more governable.
5. How MCP Is Already Used in the Industry
- LambdaTest uses MCP for AI-driven test result analysis — automatically classifying failures and linking them to Jira tickets.
- Qase employs MCP to generate and export test designs directly into executable code, eliminating manual translation.
- Tricentis Tosca launched an MCP server, allowing its AI assistant to interact with test data securely.
These are early steps toward autonomous testing systems, where AI doesn’t just assist but operates as part of the delivery pipeline.
6. How to Start Working with MCP
1. Learn the Basics
Read introductory guides from Applitools, TestCollab, or testRigor — they provide solid overviews.
2. Set Up an MCP Server
Use open-source templates like OpenAI MCP Server Template.
3. Connect Your Stack
Create a simple integration with Jira or your testing framework through the MCP API. Even a basic “fetch test results” endpoint is a huge leap.
4. Experiment
Connect your AI assistant to Jenkins or TMS and observe how it handles real test data.
5. Think About Security
Define which data your AI can access and what actions it can perform. MCP includes permission control — use it from day one.
7. What’s Next: MCP and Autonomous Testing
MCP paves the way for a new level of QA, where AI will:
- Manage test cycles.
- Analyze root causes of failures.
- Generate tests aligned with code changes.
- Learn continuously from your project data.
Within a couple of years, MCP will become as standard as REST APIs or Git. QA engineers who understand how to work with it will stay ahead of the curve.
8. Conclusion
When we at Skipper Soft first started building integrations between AI and testing tools, everything had to be done manually — prompts, adapters, hacks. MCP eliminates exactly that pain.
It turns AI testing from a collection of tricks into a systemic approach to quality.
If you want AI to become a true partner in your testing process, start by learning MCP. It’s not just a technology. It’s the language that finally lets AI speak to our quality systems.
