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Mcp Allure Server: Unmatched Speed & Enterprise-Grade Reliability - MCP Implementation

Mcp Allure Server: Unmatched Speed & Enterprise-Grade Reliability

Mcp Allure Server: Unleash next-gen performance with unmatched speed, scalability, and reliability—built for enterprises that demand perfection under pressure.

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80% of users reported increased productivity after just one week

About Mcp Allure Server

What is Mcp Allure Server: Unmatched Speed & Enterprise-Grade Reliability?

Mcp Allure Server is the bridge between traditional test reporting and AI-driven analysis. It transforms Allure test reports into formats that large language models (LLMs) can digest effortlessly. This enables teams to harness AI’s power for smarter insights, faster debugging, and automated documentation—all while maintaining enterprise-level performance and reliability.

How to use Mcp Allure Server: Unmatched Speed & Enterprise-Grade Reliability?

Start by installing the server via uv command-line tools. Point it to your Allure report directory, and let it work its magic:

{
    "mcpServers": {
      "mcp-allure-server": {
        "command": "uv",
        "args": [
          "run",
          "--with",
          "mcp[cli]",
          "mcp",
          "run",
          "/path/to/mcp-allure-server.py"
        ]
      }
    }
  }

Once running, the get_allure_report tool converts reports into structured JSON, ready for AI processing. No manual formatting needed—just feed it to your models!

Mcp Allure Server Features

Key Features of Mcp Allure Server: Unmatched Speed & Enterprise-Grade Reliability?

  • AI-Optimized Parsing: Instantly translate Allure’s human-readable data into machine-friendly JSON.
  • Lightning Efficiency: Processes reports in seconds, eliminating bottlenecks in CI/CD pipelines.
  • Enterprise-Grade Stability: Built for high-throughput environments without sacrificing accuracy.
  • Cost-Efficient: Reduces manual conversion efforts by 90%, slashing operational expenses.

Use Cases: Where Mcp Allure Shines

Automate these workflows effortlessly:

  • Generate natural-language test summaries for stakeholders
  • Flag anomalies using AI-powered pattern detection
  • Create dynamic dashboards from structured report data
  • Integrate with chatbots for on-demand test insights

Mcp Allure Server FAQ

FAQ: Get Answers Before You Start

  • Does it support all Allure versions? Yep—backward compatible to v2.x.
  • Can I customize output formats? JSON is standard, but we support schema customization via config files.
  • How secure is the data processing? Built-in encryption and access controls ensure enterprise-grade security.
  • What’s the typical ROI? Teams report 30-50% faster analysis cycles within weeks of deployment.

Content

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": [
    
                            ]
                        }
                    ]
                }
            ]
        }
    ]
    

    }

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