Using MCP and Cursor to Review Test Cases in Allure TestOps

Discover AnythingSignupWrite New StoryUsing MCP and Cursor to Review Test Cases in Allure TestOpsbyOlegbyOleg|@tankislevaQA Engineering Manager at TechVillQA, DAD, AI enthusiastSubscribeJuly 29th, 2026TLDR Your browser does not support the audio element.Speed1xVoiceDr. One Ms. Hacker byOleg@tankislevabyOleg|@tankislevaQA Engineering Manager at TechVillQA, DAD, AI enthusiastSubscribeStory's CredibilitybyOleg|@tankislevaQA Engineering Manager at TechVillQA, DAD, AI enthusiastSubscribeStory's CredibilityHi! I’m Oleg, a QA Engineering Manager focused on test strategy, automation, and AI-assisted QA workflows. My goal is simple: eliminate repetitive work for QA engineers using AI tools. In an ideal world, we provide an AI model with requirements, Figma designs, Git branches, and other project artifacts through MCP. The model then helps us: Create test cases using the available project context. Generate automated tests based on those test cases. We Decided to Start With AI Test Case Reviews Writing high-quality test cases from requirements and supporting documentation is not an easy task. Instead of trying to automate the entire process immediately, we decided to start with something smaller: an AI-powered test case reviewer. The basic idea was: A QA engineer moves a test case to the Review status in Allure TestOps. An AI agent retrieves and reviews the test case according to our internal guidelines. The agent publishes the review as a new comment on the test case. The test case is moved to an appropriate status, such as Needs Fixing, so the QA engineer immediately knows what to do next. To implement this workflow, we needed the AI agent to be able to: Connect to Allure TestOps. Retrieve a test case and its scenario. Review the content. Publish the review back to TestOps. For this purpose, we created our own MCP server for Allure TestOps, along with an interface for configuring review prompts for individual TestOps projects. Web interface for configuring AI test case review prompts Why We Did Not Use the Built-in Allure TestOps MCP Allure TestOps recently introduced its own MCP integration and an AI chat interface directly inside the product. However, the AI chat requires a separate license. We also discovered the official MCP integration only after we had already developed our own solution. After examining the built-in TestOps AI functionality, we concluded that our implementation covered our current needs at least as well. Our solution also remains free for us within our existing infrastructure and software licenses. How Our MCP Server Works Our MCP server is written in Python using the FastMCP framework. We also converted the OpenAPI specification provided by Allure TestOps into a generated API client. The resulting workflow looks like this: The MCP server calls the TestOps API. It retrieves the required test case data, including its scenario, steps, attachments, and other fields. The data is passed to an LLM for analysis. After processing, the result is saved back to TestOps through MCP—for example, as a new review comment. Allure TestOps ↓ MCP server ↓ Language model ↓ MCP server ↓ TestOps comment Why We Used Cursor When we started working on the prototype, we had several practical limitations: The web interface for reviewing test cases was not completely ready. Our MCP server could occasionally fail to return some parts of a TestOps scenario. The language model used by the main solution required separate payment. Our team already had access to corporate Cursor accounts and experience using Cursor. I therefore created a minimal project that could be opened in Cursor. The goal was to let any QA engineer: Clone the repository. Open it in Cursor. Run a single command to review a test case. Quickly evaluate the quality of the AI-generated review. How the Cursor Project Is Structured The project contains only a few components: One project rule containing the main test case review prompt. One