CI Investigator MCP

CI Investigator MCP

Provides tools to analyze and debug GitHub Actions CI failures, including summarizing failures, detecting flaky tests, and suggesting fixes.

Category
Visit Server

README

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

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:

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

On Windows, wrap npx with cmd /c:

{
  "servers": {
    "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.

IDE can't find node or npx

Some IDEs (VS Code, Cursor) launch with a limited PATH and can't find Node.js installed via nvm or similar version managers.

Run the following to find the full paths:

which node && which npx

Then use the full path in your MCP config:

{
  "servers": {
    "ci-investigator": {
      "command": "/Users/your-user/.nvm/versions/node/v22.14.0/bin/npx",
      "args": ["-y", "ci-investigator-mcp"],
      "env": {
        "GITHUB_TOKEN": "your_token",
        "PATH": "/Users/your-user/.nvm/versions/node/v22.14.0/bin:/usr/local/bin:/usr/bin:/bin"
      }
    }
  }
}

License

ISC

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured