Test Coverage MCP server

Test Coverage MCP

Make your agents coverage-aware as they code for you

“Hey, I’m a coding agent. I just created flashy nifty feature… but oops, I downgraded the coverage 🤓. How could I know that?”

“Hey, I’m a testing agent. I was tasked to cover some code with testing, but how can I find which areas are not covered?😳”

Give your coding and testing agent eyes: MCP server that provides instant, reliable, token-efficient test coverage data for any programming language (LCOV based)

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🚀 Just launched (November 2025) ! I spend great time these days on polishing this library. If you find this valuable, a ⭐ star helps signal to other developers that this project is worth their attention

The Problem

When AI coding agents work on your code without proper coverage tooling, they face three critical issues:

1. Coverage Blindness - They can't see if their changes improved or regressed test coverage
2. Token Waste - They burn thousands of tokens trying to parse massive LCOV files (some exceed 10 MB)
3. Unreliable Scripts - They improvise custom parsing scripts that often fail or produce incorrect results

The Solution

This MCP server solves all three problems by providing:

• Coverage Awareness - Agents can check coverage anytime with a simple tool call
• Token Efficiency - Get coverage summaries in <100 tokens instead of thousands
• Accuracy - Production-grade LCOV parsing that handles all format variations
• Baseline Tracking - Measure coverage progress within a session without keeping state in memory

Test Coverage: This project maintains 95% test coverage and we're targeting 100% soon.

Two Main Workflows

1. Query Coverage Summary

Ask for overall project coverage or coverage for specific files:

// Get overall project coverage
coverage_summary({ lcovPath: "./coverage/lcov.info" });
// Returns: { linesCoveragePercentage: 87.5, branchesCoveragePercentage: 82.1 }

// Get coverage for specific files
coverage_file_summary({
  lcovPath: "./coverage/lcov.info",
  filePath: "src/utils/parser.ts",
});
// Returns: { path: "src/utils/parser.ts", linesCoveragePercentage: 92.0, branchesCoveragePercentage: 88.5 }

2. Baseline Tracking for Session Progress

Establish a baseline at session start, then measure your progress:

// At session start - record current coverage as baseline
start_recording({ lcovPath: "./coverage/lcov.info" });
// Returns: "Recording started"

// ... agent writes code and tests ...

// Check coverage impact
get_diff_since_start({ lcovPath: "./coverage/lcov.info" });
// Returns: { linesPercentageImpact: +2.3, branchesPercentageImpact: +1.8 }

Why baseline tracking? Without it, agents would need to keep initial coverage in their stateful memory throughout the session, consuming valuable context window space.

Installation

npm install -g test-coverage-mcp

Configuration

Add this MCP server to your AI coding tool's configuration:

Claude Desktop (Claude Code)

macOS: Edit ~/Library/Application Support/Claude/claudedesktopconfig.json Windows: Edit %APPDATA%\Claude\claudedesktopconfig.json

{
  "mcpServers": {
    "test-coverage": {
      "command": "npx",
      "args": ["-y", "test-coverage-mcp"]
    }
  }
}

After updating, restart Claude Desktop.

Cursor IDE

Create or edit .cursor/mcp.json in your project root:

Shortened. The full README is on GitHub.