Test Coverage MCP
Test Coverage MCP is an MCP server that provides test coverage data for AI programming agents. It supports parsing LCOV format files, allowing agents to view coverage changes in real - time when coding, avoid coverage decline, and track improvement progress.
rating : 2.5 points
downloads : 6.9K
What is Test Coverage MCP?
Test Coverage MCP is a Model Context Protocol server specifically designed to provide code test coverage data for AI coding assistants. It can parse standard LCOV format coverage reports, allowing AI assistants to understand test coverage in real - time when writing code, ensuring that code quality does not decline due to the addition of new features.How to use Test Coverage MCP?
First, install the MCP server, then configure it in your AI coding tools (such as Claude Desktop, Cursor IDE, etc.). After configuration, AI assistants can query coverage data, track coverage changes through simple tool calls, and maintain attention to test coverage when writing code.Applicable scenarios
Applicable to any scenario where AI assistants are used for code development, especially: 1. When AI assistants write new feature code 2. When AI assistants write test code 3. During code refactoring or optimization 4. Projects that need to monitor code quality changesMain Features
Coverage Query
Query test coverage data for the entire project or specific files, including line coverage and branch coverage percentages.
Baseline Tracking
Record the coverage at the start of the session as a baseline, then track coverage changes to help AI assistants understand the impact of their work on coverage.
Multi - platform Support
Supports all mainstream AI coding tools, including Claude Desktop, Cursor IDE, GitHub Copilot, and Windsurf.
LCOV Format Compatibility
Supports all standard LCOV format variants and can handle coverage reports generated by different test frameworks.
Advantages
Coverage visualization: Allows AI assistants to see test coverage data and avoid blind code writing
Save Tokens: Using the MCP server can save a large number of Tokens compared to directly parsing large LCOV files
Accuracy: Uses a production - grade LCOV parser to ensure the accuracy of coverage data
Stateless design: The baseline tracking function avoids storing a large amount of data in the context of AI assistants
Limitations
Requires generating LCOV reports: You must run tests and generate coverage reports before using
Path matching: File paths must exactly match the paths in the LCOV report
Temporary storage: Baseline data is stored in the temporary directory and will be lost after the system restarts
How to Use
Install the MCP Server
Globally install the Test Coverage MCP server via npm
Configure AI Tools
Edit the corresponding configuration file according to the AI coding tool you are using and add the MCP server configuration
Generate Coverage Reports
Run your test suite and generate a coverage report in LCOV format
Start Using
Restart the AI coding tool, and now the AI assistant can query coverage data
Usage Examples
Check coverage before developing new features
Before starting to write new feature code, the AI assistant checks the current test coverage to ensure an understanding of the project's quality baseline.
Track coverage changes
During the process of writing code and tests, the AI assistant tracks coverage changes to ensure that new code does not reduce coverage.
Check the coverage of a specific file
After the AI assistant modifies a file, it checks the coverage of that file to ensure that the modification does not break the existing test coverage.
Frequently Asked Questions
Why is this MCP server needed? Can't the AI assistant parse the LCOV file by itself?
Which test frameworks are supported?
Where is the baseline data stored? Will it be permanently saved?
What if the LCOV file path is not the default./coverage/lcov.info?
Will this MCP server affect the performance of the AI assistant?
Related Resources
GitHub Repository
Project source code, issue tracking, and contribution guidelines
npm Package Page
npm package information, version history, and installation statistics
MCP Official Documentation
Official documentation and specifications for the Model Context Protocol
Issue Feedback
Report bugs, request features, or ask questions

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