CI Investigator MCP

Published MCP server for investigating GitHub Actions CI failures.

This package exposes tools to:

• list recent failed workflow runs

• summarize a failed run from logs

• compare a failed run with the previous success

• detect flaky jobs on a branch

• explain likely root cause category

• suggest remediation and validation steps

• report failure trends and CI health

• identify likely regression commit/PR

• generate failure digest for notifications

Available Tools

1) get_failed_runs

List recent failed workflow runs for a repository.

Input:

• owner (string, required): repo owner or org

• repo (string, required): repo name

• limit (number, optional, default: 10, min: 1, max: 100)

Returns:

• array of failed runs with id, workflow name, branch, short commit, URL, and timestamps

2) summarize_failure

Fetch and summarize a failed run.

Input:

• owner (string, required)

• repo (string, required)

• run_id (number, required): failed workflow run id

Returns:

• run id

• failed job name

• failed step name

• log excerpt text

Notes:

• attempts run log download first

• falls back to failed job logs

• falls back to check-run annotations when logs are unavailable

3) compare_with_last_success

Compare a failed run with the previous successful run on the same branch.

Input:

• owner (string, required)

• repo (string, required)

• run_id (number, required): failed workflow run id

Returns:

• failed run snapshot

• last successful run snapshot (or null)

• diff fields:

  • commit_changed

  • author_changed

  • event_changed

  • commits_between (GitHub compare URL or fallback text)

4) detect_flaky_tests

Detect flaky jobs by analyzing recent completed runs on a branch.

Input:

• owner (string, required)

• repo (string, required)

• branch (string, required)

• limit (number, optional, default: 30, min: 1, max: 100)

Returns:

• repository and branch metadata

• number of analyzed runs

• flaky jobs with pass/fail counts and flakiness score

5) explain_failure_root_cause

Classify likely failure cause based on logs and fallback data.

Input:

• owner (string, required)

• repo (string, required)

• run_id (number, required)

Returns:

• cause category (test_regression, infra_network, dependency, timeout, lint_or_type, auth_permissions, unknown)

• confidence and supporting evidence lines

• failed job and failed step

6) suggest_fix_for_failure

Suggest practical remediation and validation steps for a failed run.

Input:

• owner (string, required)

• repo (string, required)

• run_id (number, required)

Returns:

• classified category

• targeted suggestions

• validation checklist

7) list_failure_trends

Summarize recurring failed jobs over a configurable time window.

Input:

• owner (string, required)

• repo (string, required)

• days (number, optional, default: 14)

• branch (string, optional)

• limit (number, optional, default: 100)

Returns:

• failure totals in the selected window

• top failing jobs with occurrence counts and first/last seen timestamps

8) find_regression_pr_or_commit

Find likely regression commit and linked PR for a failed run.

Input:

• owner (string, required)

• repo (string, required)

• run_id (number, required)

Returns:

• suspect commit SHA

• compare URL from last success to failed commit

• suspected PR metadata (if available)

9) ci_health_score

Compute CI health score for a branch using pass/fail and flaky-job signals.

Input:

• owner (string, required)

• repo (string, required)

• branch (string, required)

• days (number, optional, default: 14)

• limit (number, optional, default: 100)

Returns:

• pass/failure rates

• flaky jobs count

• overall health score (0-100)

10) failure_notifications_digest

Build deduplicated digest of recent failures for alerting/triage workflows.

Input:

• owner (string, required)

• repo (string, required)

• interval_hours (number, optional, default: 24)

• branch (string, optional)

• limit (number, optional, default: 20)

Returns:

• grouped failure signatures

• occurrence counts

• latest run references and compact example summary

Related MCP server: Woodpecker CI Pipeline Analyzer

Requirements

• Node.js 20+

• GitHub token in environment

Recommended token permissions:

• actions:read

• checks:read

• contents:read

Using an MCP Client

Running a server on its own is less useful than wiring it into an MCP client.

Set GITHUB_TOKEN in your client config:

{
  "mcpServers": {
    "ci-investigator": {
      "command": "npx",
      "args": ["-y", "ci-investigator-mcp"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token"
      }
    }
  }
}

On Windows, wrap npx with cmd /c:

{
  "mcpServers": {
    "ci-investigator": {
      "command": "cmd",
      "args": ["/c", "npx", "-y", "ci-investigator-mcp"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token"
      }
    }
  }
}

Troubleshooting

• 401 or 403 errors: verify GITHUB_TOKEN and permissions.

• Empty or partial logs: some workflows/log artifacts can be unavailable; the server uses fallback strategies.

• Run not found: confirm run_id, owner, and repo are correct.

License

ISC