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MCP-Allure는 Allure 보고서를 읽고 LLM 친화적인 형식으로 반환하는 MCP 서버입니다.

동기 부여

AI와 대규모 언어 모델(LLM)이 소프트웨어 개발에 점점 더 중요해짐에 따라, 기존 테스트 보고와 AI 지원 분석 간의 격차를 메울 필요성이 커지고 있습니다. 기존 Allure 테스트 보고서 형식은 사람이 읽을 수 있지만 LLM 사용 및 처리에 최적화되어 있지 않습니다.

MCP-Allure는 Allure 시험 보고서를 LLM 친화적인 형식으로 변환하여 이러한 과제를 해결합니다. 이러한 변환을 통해 AI 모델은 시험 결과를 더 잘 이해하고 분석하며 통찰력을 제공할 수 있게 되어 다음과 같은 작업을 더 쉽게 수행할 수 있습니다.

  • 의미 있는 테스트 요약 및 통찰력 생성

  • 테스트 실패 패턴 식별

  • 실패한 테스트에 대한 잠재적인 수정 사항 제안

  • 더욱 효과적인 AI 지원 디버깅을 활성화하세요

  • 자동화된 테스트 문서 생성을 용이하게 합니다.

MCP-Allure는 LLM 소비를 위한 테스트 보고서를 최적화하여 개발팀이 테스트 워크플로에서 AI 도구의 모든 잠재력을 활용할 수 있도록 돕고, 이를 통해 더욱 효율적이고 지능적인 테스트 분석과 유지 관리를 가능하게 합니다.

문제 해결

  • 효율성 : 기존 테스트 보고 형식은 AI 소비에 최적화되어 있지 않아 테스트 분석 및 유지 관리가 비효율적입니다.

  • 정확성 : AI 모델은 AI 소비에 최적화된 형식이 아닌 테스트 보고서를 해석하고 분석하는 데 어려움을 겪을 수 있습니다.

  • 비용 : 시험 보고서를 LLM 친화적인 형식으로 변환하는 데는 시간이 많이 걸리고 비용도 많이 듭니다.

주요 특징

  • 변환 : Allure 시험 보고서를 LLM 친화적인 형식으로 변환합니다.

  • 최적화 : AI 소비를 위해 테스트 보고서를 최적화합니다.

  • 효율성 : 테스트 보고서를 효율적으로 변환합니다.

  • 비용 : 저렴한 비용으로 테스트 보고서를 변환합니다.

  • 정확도 : 높은 정확도로 테스트 보고서를 변환합니다.

설치

uv를 사용하여 mcp-repo2llm을 설치하려면:

지엑스피1

도구

겟_얼루어_리포트

  • Allure 보고서를 읽고 JSON 데이터를 반환합니다.

  • 입력:

    • report_dir: Allure HTML 보고서 경로

  • 반품:

    • 문자열, 다음과 같이 포맷된 JSON 데이터:

{
    "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": [

                            ]
                        }
                    ]
                }
            ]
        }
    ]
}

Available Tools

1 tool
get_allure_reportC

read allure report and return json data

ParametersJSON Schema
NameRequiredDescriptionDefault
results_dirYes

TDQS

C2.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions reading and returning JSON data, but fails to disclose critical traits: whether this is a read-only operation (implied by 'read' but not explicit), potential side effects (e.g., file access permissions), error handling, or performance aspects (e.g., large report processing). The description adds minimal value beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise—a single sentence with no wasted words—and front-loaded with the core action. However, it is under-specified rather than efficiently concise; it could benefit from slightly more detail without losing brevity. It earns a high score for structure but loses a point due to under-specification.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (a tool to read and parse reports), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It does not explain the return format beyond 'json data' (e.g., structure, keys), error conditions, or dependencies. For a tool with one parameter and no structured support, more context is needed to be adequately helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, with 1 parameter ('results_dir') undocumented in the schema. The description does not compensate by explaining what 'results_dir' means (e.g., path to Allure results directory, expected format), its role in reading the report, or any constraints. This leaves the parameter's semantics unclear, failing to add meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'read[s] allure report and return[s] json data', which provides a basic verb+resource combination ('read allure report') and outcome ('return json data'). However, it's vague about what constitutes an 'allure report' (e.g., test results, specific file format) and doesn't differentiate from siblings (though none exist). It avoids tautology by not merely restating the name, but lacks specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool—there are no alternatives mentioned (sibling tools are none), no context about prerequisites (e.g., requires an existing report directory), and no exclusions or comparisons. The description implies usage for reading reports but offers no further direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedget_allure_report

TDQS

C2.7/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'get_allure_report' has a single, clearly defined purpose of reading Allure reports and returning JSON data.

Naming Consistency5/5

The single tool name 'get_allure_report' follows a clear verb_noun pattern (get + report). Since there is only one tool, consistency is inherently perfect with no deviations to assess.

Tool Count2/5

The server has only one tool, which feels thin for a reporting domain like Allure. A single tool for reading reports lacks essential operations such as generating, listing, or managing reports, making it under-scoped for typical use cases.

Completeness2/5

The tool surface is severely incomplete for an Allure reporting server. It only provides a 'get' operation for reports, missing critical functions like creating reports, listing available reports, updating report data, or deleting reports, which are essential for full lifecycle coverage.

Maintenance

ActivityInactive
ResponsivenessNo issues

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