소개
AI coding agents frequently encounter challenges in assessing the impact of their code changes on test coverage, often leading to regressions, inefficient LCOV file parsing that consumes excessive tokens, and unreliable custom scripts. This Model Context Protocol (MCP) server directly addresses these pain points by offering agents real-time, dependable, and token-efficient test coverage insights. It meticulously parses LCOV files using a robust, production-grade parser, delivers both overall project and granular file-specific coverage summaries, and includes a baseline tracking feature to monitor progress within a coding session, thereby enabling agents to make informed, coverage-aware decisions.
