What is Mcp Allure

MCP-Allure is a MCP server that reads Allure reports and transforms them into formats that are friendly for Large Language Models (LLMs).

Use cases

Use cases for MCP-Allure include generating test summaries, identifying patterns in test failures, suggesting fixes, enabling AI-assisted debugging, and automating test documentation.

How to use

To use MCP-Allure, install it via the provided command and run it to convert Allure test reports into LLM-friendly formats for analysis and insights.

Key features

Key features of MCP-Allure include conversion of Allure reports to LLM-friendly formats, optimization for AI consumption, efficient processing, low-cost conversion, and high accuracy in report transformation.

Where to use

MCP-Allure can be used in software development environments where AI-assisted testing and analysis are required, particularly in teams utilizing Allure for test reporting.

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

Installing via Smithery

To install MCP-Allure for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @crisschan/mcp-allure --client claude

Installing Manually

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": [

                            ]
                        }
                    ]
                }
            ]
        }
    ]
}