Analyzes codebases to produce structured quality health reports with risk-scored modules based on git churn, test coverage, and test-to-source mapping, acting as an MCP server for AI coding agents and a standalone CLI.
MCP server for test impact analysis and code intelligence. Maps tests to code and git history to determine impacted tests, risk scores, and ownership for AI coding agents.
This MCP server enables automated maintenance and code analysis for Python/pytest repositories in isolated Docker environments. It supports read-only investigations, fix-and-verify tasks, and provides full audit trails with SQLite event history and artifact exports.
This MCP server builds a local flakiness knowledge graph from Playwright test run history and enables AI agents to query flaky tests, failure patterns, trends, and correlated git commits, helping diagnose test reliability without manual analysis.
A production-ready MCP server that provides comprehensive dbt project quality assessment for any GitHub repository, enabling AI agents to analyze dbt models, check metadata coverage, and map data lineage.
A universal AI-powered testing server built on the Model Context Protocol (MCP). Allows AI agents to inspect, execute, test, monitor, debug, and report on software projects.
