This PyTorch HUD API implementation provides a Python library and MCP server for accessing PyTorch's CI/CD analytics data, enabling developers to investigate build failures and trunk health issues. Built with FastMCP, it offers tools for retrieving workflow and job information, analyzing large log files efficiently, executing ClickHouse queries against CI metrics, and monitoring resource utilization. The server exposes both synchronous and asynchronous functions through a standardized interface, making it valuable for PyTorch contributors debugging CI failures, investigating test flakiness, or analyzing performance trends across the CI infrastructure.
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Highlight AIBasic commit info without jobs
Aggregated job status counts
Jobs with filtering by status/workflow/name
Failed jobs with detailed failure info
Status for recent commits with job statistics
Download logs to local storage
Find errors, warnings, etc.
Parse test execution results
Extract specific log sections
Search across multiple logs
