Allure Test Reports
About
Provides a bridge to Allure test reports, enabling access to test execution data including test cases, steps, statuses, and timestamps through a structured API for analysis and custom reporting.
Details
- Author
- crisschan
- Repository
- crisschan/mcp-allure
- GitHub stars
- 1
- Downloads
- 381
- License
- Apache License 2.0
- Categories
- Developer Tools, Other, Productivity, Design, Workplace, AI, Knowledge Base, Communication, Frontend
Jump to
- 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.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Allure Test ReportsCommand (node, npx, python, etc.)uvArguments-
Argument 1
run -
Argument 2
--with -
Argument 3
mcp[cli] -
Argument 4
mcp -
Argument 5
run -
Argument 6
/Users/crisschan/workspace/pyspace/mcp-allure/mcp-allure-server.py
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
-
Argument 1
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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"
]
}
}
}
get_allure_report
Reads Allure report and returns JSON data. Input: report_dir (string) - Allure HTML report path. Returns: String, formatted JSON data.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"allure test reports": {
"cwd": null,
"env": {},
"args": [
"run",
"--with",
"mcp[cli]",
"mcp",
"run",
"/Users/crisschan/workspace/pyspace/mcp-allure/mcp-allure-server.py"
],
"shell": false,
"command": "uv"
}
}
}
Linux
{
"cwd": null,
"env": [],
"args": [
"run",
"--with",
"mcp[cli]",
"mcp",
"run",
"/Users/crisschan/workspace/pyspace/mcp-allure/mcp-allure-server.py"
],
"shell": false,
"command": "uv"
}
Macos
{
"cwd": null,
"env": [],
"args": [
"run",
"--with",
"mcp[cli]",
"mcp",
"run",
"/Users/crisschan/workspace/pyspace/mcp-allure/mcp-allure-server.py"
],
"shell": false,
"command": "uv"
}
Windows
{
"cwd": null,
"env": [],
"args": [
"run",
"--with",
"mcp[cli]",
"mcp",
"run",
"/Users/crisschan/workspace/pyspace/mcp-allure/mcp-allure-server.py"
],
"shell": false,
"command": "uv"
}
MCP-Allure is a MCP server that reads Allure reports and returns them in LLM-friendly formats.
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.
- 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.
- 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.
{ "mcpServers": { "mcp-allure-server": { "command": "uv", "args": [ "run", "--with", "mcp[cli]", "mcp", "run", "/Users/crisschan/workspace/pyspace/mcp-allure/mcp-allure-server.py" ] } } }
- Reads Allure report and returns JSON data
- Input:
- report_dir: Allure HTML report path
{ "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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