An open-source entry in TopGit's GitHub warehouse: goldbergyoni/test-coverage-mcp, 41 stars, TypeScript. Context engineering: Make your agents aware of how they affect 🧪 test coverage🧪 during a coding session
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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:
Coverage Blindness - They can't see if their changes improved or regressed test coverage
Token Waste - They burn thousands of tokens trying to parse massive LCOV files (some exceed 10 MB)
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:
Agent: "Let me check the current test coverage before I start working"
[Uses coverage_summary tool]
Agent: "Current coverage is 87.5% lines and 82.1% branches. I'll aim to maintain or improve this."
Example 2: Track Coverage Impact During Development
Agent: "I'll record the baseline coverage first"
[Uses start_recording tool]
Agent: "Now I'll add the new authentication feature with tests"
[Writes code and tests]
Agent: "Let me check the coverage impact"
[Uses get_diff_since_start tool]
Agent: "Great! Coverage increased by 2.3% for lines and 1.8% for branches."
Example 3: Verify Specific File Coverage
Agent: "Let me check coverage for the file I just modified"
[Uses coverage_file_summary with filePath: "src/auth/validator.ts"]
Agent: "The validator.ts file now has 95% line coverage and 92% branch coverage."
How It Works
This MCP server:
Parses LCOV files using a production-grade parser that handles all LCOV format variations
Calculates coverage percentages for overall project or individual files
Stores baselines in a temporary directory for session-based tracking
Returns compact JSON responses that consume minimal tokens
LCOV Format Support
This server supports all standard LCOV file formats, including:
Files with summary sections (SF:, end_of_record)
Files with line-by-line data only (DA: entries)
Files with branch coverage data (BRDA:, BRF:, BRH:)
Mixed formats within the same file
Troubleshooting
"LCOV file not found"
Ensure you've run your test suite with coverage enabled first
Check that the path to your LCOV file is correct (relative paths are resolved from current working directory)
Default path is ./coverage/lcov.info
"No coverage data found for file"
Verify the file path matches exactly as it appears in the LCOV file
Some test frameworks use absolute paths, others use relative paths
"No baseline recording found"
You must call start_recording before calling get_diff_since_start
Baselines are stored in temporary storage and cleared when the system restarts
Development
# Install dependencies
npm install
# Build
npm run build
# Run tests (with coverage!)
npm test
# Run linter
npm run lint
# Test with MCP inspector
npm run inspect
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
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What is goldbergyoni/test-coverage-mcp?
goldbergyoni/test-coverage-mcp (goldbergyoni/test-coverage-mcp) is a TypeScript project on GitHub. From the project's own README: Context engineering: Make your agents aware of how they affect 🧪 test coverage🧪 during a coding session
What language is goldbergyoni/test-coverage-mcp written in?
goldbergyoni/test-coverage-mcp is written primarily in TypeScript. GitHub's language field is based on the largest share of bytes in the default branch.
Read full README in the tab above.
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