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": [
]
}
]
}
]
}
]
}
Allure Report Transformer
Project Details
- crisschan/mcp-allure
- Apache License 2.0
- Last Updated: 3/25/2025
Recomended MCP Servers
Kokoro Text to Speech (TTS) MCP Server
A Model Context Protocol (MCP) server that provides tools to query Erick Wendel's contributions across different platforms
A universal RPC layer for AI agents. Connect to any function, any language, any framework, in minutes.
Zendesk MCP Server
Add Obsidian integrations like semantic search and custom Templater prompts to Claude or any MCP client.
Enables Collaborative UX in Claude Desktop
Super Shell MCP
Config files for my GitHub profile.
🔎 A MCP server for Unsplash image search.





