MCP-Allure
MCP-アリュール
MCP-Allure は、Allure レポートを読み取り、LLM 対応の形式で返す MCP サーバーです。
モチベーション
AIと大規模言語モデル(LLM)がソフトウェア開発にますます不可欠なものとなるにつれ、従来のテストレポートとAI支援分析の間のギャップを埋める必要性が高まっています。従来のAllureテストレポート形式は人間が読める形式ですが、LLMでの利用と処理には最適化されていません。
MCP-Allureは、AllureのテストレポートをLLM対応形式に変換することで、この課題に対処します。この変換により、AIモデルはテスト結果をより深く理解、分析し、洞察を提供できるようになり、以下のことが容易になります。
有意義なテストの概要と洞察を生成する
テスト失敗のパターンを特定する
失敗したテストの潜在的な修正を提案する
より効果的なAI支援デバッグを可能にする
自動テストドキュメント生成を容易にする
MCP-Allure は、LLM の使用に合わせてテスト レポートを最適化することで、開発チームがテスト ワークフローで AI ツールの可能性を最大限に活用できるようにし、より効率的でインテリジェントなテスト分析とメンテナンスを実現します。
問題解決
効率: 従来のテスト レポート形式は AI の使用に最適化されていないため、テスト分析とメンテナンスの効率が低下します。
精度: AI モデルは、AI の使用に最適化されていない形式になっているテスト レポートの解釈と分析に苦労する可能性があります。
コスト: テスト レポートを LLM 対応形式に変換するには、時間がかかり、コストもかかる場合があります。
主な特徴
変換: Allure テスト レポートを LLM 対応形式に変換します。
最適化: AI の使用に合わせてテスト レポートを最適化します。
効率: テスト レポートを効率的に変換します。
コスト: 低コストでテストレポートを変換します。
精度: テストレポートを高精度で変換します。
インストール
uv を使用して mcp-repo2llm をインストールするには:
{
"mcpServers": {
"mcp-allure-server": {
"command": "uv",
"args": [
"run",
"--with",
"mcp[cli]",
"mcp",
"run",
"/Users/crisschan/workspace/pyspace/mcp-allure/mcp-allure-server.py"
]
}
}
}道具
ゲットアリュールレポート
Allureレポートを読み取り、JSONデータを返します
入力:
report_dir: Allure HTML レポートのパス
戻る:
次のような形式の JSON 形式の文字列データ:
{
"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 toolget_allure_reportC
read allure report and return json data
| Name | Required | Description | Default |
|---|---|---|---|
| results_dir | Yes |
TDQS
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.
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.
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.
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.
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.
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 tool update
v1.0.0- First observed
get_allure_report
TDQS
Scored across 1 tool
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.
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.
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.
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.
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