Horshchar took an unusual route into QA, moving from technical support into engineering automation — most specialists in the field come to QA from academic backgrounds or adjacent technical roles. Kateryna is currently pursuing a Master of Science in Software Engineering at Quantic School of Business and Technology in the United States. She has also authored five peer-reviewed papers on software testing methodology published in international scientific journals in 2025–2026.
TeamCity QA Intelligence automatically identifies why an automated test failed in TeamCity and classifies the cause into one of three categories: a code change (linked to the specific commit and its author), instability of the test itself, or an infrastructure problem on the server. Each diagnosis includes recommendations on how to fix the issue. The solution is universal by design: the system does not care who wrote the test or what type it is. That makes the tool equally useful for QA engineers and for developers working with unit and integration tests. The code is open source and available in the kate-horshchar/teamcity-qa-mcp repository on GitHub.
The problem the tool addresses is industry-wide. Internal research at Google found that roughly 84% of cases in which automated tests suddenly start failing are caused not by real product bugs, but by instability in the tests themselves. Microsoft has estimated the average investigation to take around 30 minutes — and Google has reported that roughly 16% of its tests show some level of flakiness, which at scale turns individual investigations into hours of triage every week.
The idea came out of Kateryna’s day-to-day work at SchoolDay, a U.S. EdTech provider whose platform acts as a secure intermediary between schools and external educational services. SchoolDay moves data between more than 36,000 schools and over 3,000 school districts across the United States, including personal student data protected under federal regulation (FERPA). She is the sole engineer responsible for automated testing across two of the company’s core integration modules.
The stakes are high on both operational and regulatory fronts. The reliability of the platform determines whether class rosters, grades, and schedules reach classroom gradebooks and educational applications across thousands of schools on time. Every integration failure also creates potential exposure of student personal data and a FERPA violation for which the company is answerable to regulators. In this environment, automated testing serves as a daily safeguard on both fronts.
“It was routine that kept repeating, but one case was the turning point. On top of my main tasks, I spent more than a week trying to find the cause of tests that were failing for one particular feature. The company had provided us with corporate access to Claude, but that wasn’t enough: I had to copy stack traces manually, explain the problem, and the model was constantly missing the context,” Horshchar says. “Tools like this already exist for other CI systems. I started looking for something like that for TeamCity, something like MCP in Claude Code. I couldn’t find it. And that’s how TeamCity QA Intelligence was born.”
Model Context Protocol is an open standard introduced by Anthropic in November 2024. Put simply, MCP gives an AI model direct access to the internal data of a working system, so engineers don’t have to copy fragments into a chat window. Over the following year the protocol was adopted by OpenAI, Google DeepMind, and Microsoft; in December 2025 Anthropic transferred it to the Agentic AI Foundation under the Linux Foundation. By the end of the year, the ecosystem included more than 10,000 active public MCP servers. In the industry, MCP has been dubbed “the USB-C of AI.”
TeamCity QA Intelligence is built around nineteen read-only tools that collect and normalize data from TeamCity — from build failures and log excerpts to test history and cross-project failure analysis — plus a Claude plugin with seven ready-to-use commands covering typical scenarios, from failing-build analysis to report generation. The plugin is deployed at the organizational level, and updates propagate in a single click — engineers don’t have to configure anything on their end.
The hardest part was teaching the system to work with large volumes of data. TeamCity produces far too much information for a language model to process directly. Kateryna built her own tools that automatically extract what matters most — clustered failures, relevant log fragments, recent code changes, and comparisons with the last successful run — and pass a compact, ready-to-analyze context to the model.
The choice of MCP was deliberate for two reasons. First, the server runs locally: company data never leaves the working environment, unlike commercial SaaS alternatives. Second, MCP does not lock the system to a single AI client — the server works with any MCP-compatible client, including Claude, Cursor, VS Code Copilot, and JetBrains AI.
Two examples illustrate the impact. In one instance — the “disappearing tests” problem, where the number of executed tests in a build kept drifting slightly and the team couldn’t tell where the checks were “going” — a manual investigation would have taken two to three working days. With the system, it took 20 to 40 minutes. In another case, database tests were failing every day for a week; the cause turned out to be unrelated to the product code: Java on the server had been upgraded to a new version, and one of the supporting libraries was incompatible with it. Manual analysis would have taken two to five hours; with the system, the cause was identified in roughly an hour. Her team has been using the tool daily for more than four months.
There is no direct equivalent on the market. TeamCity’s built-in feature marks a test as flaky but does not explain why. Generic MCP servers for CI/CD platforms read logs but lack QA-specific semantics. The public MCP registry lists thousands of servers for GitHub, Slack, Google Drive, and dozens of other services, but none are specialized for QA diagnosis in TeamCity.
Horshchar’s contribution to software quality was recognized with the GLOBAL100 Reputation Awards 2024 in the “Outstanding Contribution to Software Quality & Data Integration” category. Because TeamCity is widely used by U.S. engineering teams, any American company can adopt her solution for free.
“I think that within a year or two, the normal picture will become a ready-made diagnosis right next to the red build: what failed, why, and what to do about it,” she says. “Engineers’ time will go into fixing problems rather than searching for them — the search is exactly the part that should be automated. And this concerns not only testers — developers benefit just as much.”
Her next step is to teach the system to retain the history of past diagnoses within each project, improving accuracy for the team over time.
Beyond that, Kateryna Horshchar plans to actively push the tool to the wider international engineering community. The roadmap includes technical write-ups on Dev.to and LinkedIn, threads in specialized Reddit communities such as r/QualityAssurance, and direct outreach to teams already running TeamCity. The GitHub repository is also expanding, with extended documentation, configuration examples, and use cases meant to lower the barrier to entry for new users.
The potential audience extends well beyond a single professional group or region. TeamCity is used by thousands of engineering teams worldwide, ranging from small startups to large corporations in fintech, retail, healthcare, and educational technology. Its public customers include major enterprises across gaming, consumer electronics, banking, and telecommunications. For any of these teams, TeamCity QA Intelligence offers a way to reclaim the engineering hours currently spent manually triaging unstable tests. The solution will be especially valuable for regulated industries where high code-quality standards go hand in hand with strict data-protection requirements — and that is why running the server locally, without transferring data to third-party services, is a key architectural advantage.
