MCP server for reading Extent Reports in LLM-friendly format
Project description
Extent Report MCP Server
This is a Model Context Protocol (MCP) server that enables AI assistants (like Claude, Cursor, and QAFlow) to read, analyze, and understand Extent Reports.
Features
- Parse Extent Reports (JSON and Spark HTML formats)
- Extract test summaries (Pass/Fail/Skip rates)
- Retrieve detailed failure analysis including stack traces
- Compare reports to identify regressions
Installation
# Install with uv (recommended)
uv tool install mcp-extent
# Or install with pip
pip install mcp-extent
Usage (Local Development)
Since you are running this from source, add the total path to your configuration:
{
"mcpServers": {
"mcp-extent": {
"command": "uv",
"args": [
"run",
"--directory",
"D:/mcp-extent",
"mcp-extent"
]
}
}
}
Note: Adjust the path
D:/mcp-extentif you move the project.
Tools
get_extent_report: Parse full reportget_test_summary: Get high-level statsget_failed_tests: Get detailed failure infoanalyze_failures: Pattern matching for failurescompare_reports: Compare two report runs
Project details
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