CI Investigator MCP
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Related Servers
- FlicenseNot gradedqualityNot gradedmaintenanceA utility that helps diagnose and fix GitHub Actions workflow failures by analyzing run logs, identifying common failure patterns, and suggesting specific fixes through a structured decision tree.1-
- FlicenseAqualityBmaintenanceEnables CI/CD failure triage for GitHub Actions by inspecting failed runs, classifying root causes with LLM assistance, retrieving relevant runbook guidance, estimating flakiness, and either preparing dry-run fix PRs or publishing structured diagnoses to Discord for human review.2-
- AlicenseNot gradedqualityDmaintenanceAnalyzes GitHub Actions workflows and performance, helping identify bottlenecks, failures, and optimization opportunities in CI/CD pipelines.18 npmMIT
- FlicenseBqualityDmaintenanceProvides tools to analyze test failures, cluster similar failures, and detect flaky tests from input or log files, helping QA teams debug and triage issues.3-
- FlicenseAqualityBmaintenanceEnables AI assistants to summarize Playwright test runs, identify flaky tests, heuristically triage failures, generate Playwright test stubs, and retrieve recent GitHub CI status through MCP tool calls.5-
- AlicenseNot gradedqualityDmaintenanceEnables automated analysis of Woodpecker CI pipeline failures with intelligent error detection and fix suggestions. Supports both direct pipeline analysis and IDE-integrated git-context analysis using repository names, PR numbers, or branch information.6 npm3ISC
TDQS
Scored across 10 tools
Each tool has a clearly distinct purpose, covering different aspects of CI failure analysis without overlap. For example, ci_health_score computes a health metric, while detect_flaky_tests analyzes flakiness, and explain_failure_root_cause classifies root causes.
Tool names follow a consistent snake_case pattern and mostly use verb_noun or verb_phrase structure (e.g., detect_flaky_tests, list_failure_trends). One minor deviation is ci_health_score, which is a noun phrase, but it still fits the pattern.
With 10 tools, the server is well-scoped for its purpose of CI failure investigation. This count allows comprehensive coverage without being overwhelming.
The tool set covers the full lifecycle of CI failure analysis: identification (get_failed_runs), summarization (summarize_failure), root cause analysis (explain_failure_root_cause, find_regression_pr_or_commit), trend detection (list_failure_trends, detect_flaky_tests), and remediation (suggest_fix_for_failure, ci_health_score). No obvious gaps.