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": [
]
}
]
}
]
}
]
}
Metadata
Release files for iflow-mcp_mcp-allure 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| iflow_mcp_mcp_allure-0.1.0.tar.gz | 8.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| iflow_mcp_mcp_allure-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 17.5 kB
Release files / iflow_mcp_mcp_allure-0.1.0.tar.gz
| Download URL | iflow_mcp_mcp_allure-0.1.0.tar.gz |
|---|---|
| Size | 8.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.6.9
|
Release files / iflow_mcp_mcp_allure-0.1.0-py3-none-any.whl
| Download URL | iflow_mcp_mcp_allure-0.1.0-py3-none-any.whl |
|---|---|
| Size | 9.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.6.9
|