This server converts Allure test reports into LLM-friendly JSON format for AI-assisted test analysis.
Read and parse Allure reports: Processes Allure test reports from a specified directory using the
get_allure_reporttoolExtract comprehensive test data: Captures hierarchical test information including test suites, test cases, steps, labels, parameters, attachments, status (passed/failed), timestamps, severity levels, titles, and descriptions
Enable AI-powered analysis: Transforms human-readable Allure reports into structured JSON optimized for LLM consumption to generate summaries, identify failure patterns, suggest fixes, and assist with debugging
Support automated documentation: Facilitates automated test documentation generation through structured data output
MCP-Allure
MCP-Allure is a MCP server that reads Allure reports and returns them in LLM-friendly formats.
Motivation
As AI and Large Language Models (LLMs) become increasingly integral to software development, there is a growing need to bridge the gap between traditional test reporting and AI-assisted analysis. Traditional Allure test report formats, while human-readable, aren't optimized for LLM consumption and processing.
MCP-Allure addresses this challenge by transforming Allure test reports into LLM-friendly formats. This transformation enables AI models to better understand, analyze, and provide insights about test results, making it easier to:
Generate meaningful test summaries and insights
Identify patterns in test failures
Suggest potential fixes for failing tests
Enable more effective AI-assisted debugging
Facilitate automated test documentation generation
By optimizing test reports for LLM consumption, MCP-Allure helps development teams leverage the full potential of AI tools in their testing workflow, leading to more efficient and intelligent test analysis and maintenance.
Problems Solved
Efficiency: Traditional test reporting formats are not optimized for AI consumption, leading to inefficiencies in test analysis and maintenance.
Accuracy: AI models may struggle with interpreting and analyzing test reports that are not in a format optimized for AI consumption.
Cost: Converting test reports to LLM-friendly formats can be time-consuming and expensive.
Key Features
Conversion: Converts Allure test reports into LLM-friendly formats.
Optimization: Optimizes test reports for AI consumption.
Efficiency: Converts test reports efficiently.
Cost: Converts test reports at a low cost.
Accuracy: Converts test reports with high accuracy.
Installation
To install mcp-repo2llm using uv:
Tool
get_allure_report
Reads Allure report and returns JSON data
Input:
report_dir: Allure HTML report path
Return:
String, formatted JSON data, like this: