what is MCP-Allure?
MCP-Allure is a server that transforms Allure test reports into formats that are friendly for Large Language Models (LLMs), enabling better analysis and insights from test results.
how to use MCP-Allure?
To use MCP-Allure, install it using the provided command and run the server to read Allure reports. You can then input the path to your Allure HTML report to get the data in JSON format.
key features of MCP-Allure?
- Converts Allure test reports into LLM-friendly formats.
- Optimizes reports for AI consumption.
- Provides efficient and accurate report conversion.
- Low-cost conversion process.
use cases of MCP-Allure?
- Generating meaningful summaries and insights from test results.
- Identifying patterns in test failures for better debugging.
- Facilitating automated documentation generation for tests.
FAQ from MCP-Allure?
- Can MCP-Allure handle all types of Allure reports?
Yes! MCP-Allure is designed to work with standard Allure reports.
- Is there a cost associated with using MCP-Allure?
No, the conversion process is designed to be low-cost and efficient.
- How accurate is the report conversion?
MCP-Allure aims for high accuracy in converting reports, ensuring that the data is reliable for AI analysis.
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:
{
"mcpServers": {
"mcp-allure-server": {
"command": "uv",
"args": [
"run",
"--with",
"mcp[cli]",
"mcp",
"run",
"/Users/crisschan/workspace/pyspace/mcp-allure/mcp-allure-server.py"
]
}
}
}
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:
{
"test-suites": [
{
"name": "test suite name",
"title": "suite title",
"description": "suite description",
"status": "passed",
"start": "timestamp",
"stop": "timestamp",
"test-cases": [
{
"name": "test case name",
"title": "case title",
"description": "case description",
"severity": "normal",
"status": "passed",
"start": "timestamp",
"stop": "timestamp",
"labels": [
],
"parameters": [
],
"steps": [
{
"name": "step name",
"title": "step title",
"status": "passed",
"start": "timestamp",
"stop": "timestamp",
"attachments": [
],
"steps": [
]
}
]
}
]
}
]
}