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Run local QA checks for APIs, test cases, errors, and SLA evaluation.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "qaLabMcp" yet — see the docs or source repo.
Generate comprehensive QA test cases for this user registration API. Cover happy path, boundary values, invalid inputs, auth failures, and concurrency scenarios, then group them by priority.
A structured list of test cases with scenario descriptions, inputs, expected results, and priorities.
Validate whether these API responses meet field constraints and status code rules, then determine whether the response time data satisfies an SLA of P95 under 800ms and list failures.
Validation results for responses, P95 statistics, SLA pass/fail status, and details of any exceptions.
Classify this batch of HTTP error logs into client errors, server errors, and retryable issues. If customer identifiers appear in the logs, look up the matching customer records and summarize the impact.
A summary of error classifications, retry recommendations, linked customer information, and impact scope.
MCP tool for QA workflows, testing, defect tracking, and reporting.
Analyze test reports across runs to find regressions, fixes, and persistent failures.
Run browser-based web app QA tests with Playwright and generate reports.
Enforce configurable QA strategies so AI-generated tests meet strict standards.
Analyze and fix test failures with plain-language guidance in IDE and Slack.
Automate QA with generated scenarios, Playwright tests, execution, and GitHub bug reports.