GitHub Actions MCP Server

GitHub Actions MCP Server

An intelligent MCP server that gives AI agents full control over GitHub Actions CI/CD pipelines, including real-time monitoring, log analysis, AI-powered failure diagnosis, and deployment management.

Category
Visit Server

README

GitHub Actions MCP Server

An intelligent MCP (Model Context Protocol) server that gives AI agents full control over GitHub Actions CI/CD pipelines — including real-time monitoring, log analysis, AI-powered failure diagnosis, and deployment management.


Why This Exists

Most GitHub MCP servers let AI agents read files, create issues, and manage pull requests.
But they can't touch your CI/CD pipelines.

Capability Existing GitHub MCPs This Server
List workflow runs
Fetch execution logs
Diagnose failures with AI
Rerun / cancel workflows
Track deployments & rollback
Monitor repos in real time

This server closes that gap — enabling autonomous DevOps agents that can monitor, diagnose, and act on your pipelines without human intervention.


Features

  • 13 purpose-built MCP tools covering the full CI/CD lifecycle
  • AI-powered failure analysis — sends parsed logs to Claude, returns root cause + suggested fixes + severity
  • Real-time pipeline monitoring — polling-based watcher with configurable interval
  • Full workflow control — rerun (all or failed jobs only), cancel, watch until completion
  • Deployment management — list environments, track statuses, trigger rollbacks
  • Artifact handling — list and get pre-signed download URLs
  • Secure by design — tokens never logged or exposed in responses
  • Docker-ready — multi-stage build for production deployment

Demo

Ask Claude (or any MCP-compatible AI agent):

"Why did my CI pipeline fail on the main branch?"

The agent will automatically:

  1. Fetch the latest failed workflow run
  2. Retrieve and parse the execution logs
  3. Send the error context to Claude for analysis
  4. Return a structured diagnosis:
{
  "probableCause": "npm peer dependency conflict between react@18 and testing-library@13",
  "suggestedFixes": [
    "Add --legacy-peer-deps to your npm install command",
    "Upgrade @testing-library/react to v14",
    "Pin react to v17 until dependencies are resolved"
  ],
  "severity": "high",
  "diagnosticReport": "The build failed during dependency installation due to an unresolvable peer conflict introduced in the last commit. This is a common issue when mixing React 18 with older testing utilities."
}

Prerequisites


Quick Start

# 1. Clone the repository
git clone https://github.com/muhammedehab35/GITOPS-MCP/tree/main
cd github-actions-mcp

# 2. Install dependencies
npm install

# 3. Configure environment
cp .env.example .env
# Fill in GITHUB_TOKEN and ANTHROPIC_API_KEY in .env

# 4. Build
npm run build

# 5. Test with MCP Inspector
npx @modelcontextprotocol/inspector node dist/index.js

Connect to Claude Desktop

Find your config file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Add this configuration:

{
  "mcpServers": {
    "github": {
      "command": "node",
      "args": ["/absolute/path/to/github-actions-mcp/dist/index.js"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token_here",
        "ANTHROPIC_API_KEY": "sk-ant-your_key_here",
        "POLLING_INTERVAL_MS": "30000",
        "LOG_LEVEL": "info"
      }
    }
  }
}

Restart Claude Desktop. You'll see the 🔨 tools icon — the server is connected.


Available Tools

Repositories

Tool Description
github_list_repositories List all repositories accessible with your token, with language, stars, and visibility

Workflows

Tool Description
github_list_workflows List all GitHub Actions workflows defined in a repository
github_list_workflow_runs List recent runs with filters for status, branch, and count
github_get_workflow_run Get full details of a run including all job statuses and timing
github_watch_workflow Poll a run every 10s until completion or 5-minute timeout

Logs & Analysis

Tool Description
github_get_workflow_logs Fetch and parse per-job logs with automatic error extraction
github_analyze_failure AI diagnosis — root cause, fixes, severity, and diagnostic report via Claude

Actions

Tool Description
github_rerun_workflow Rerun a workflow — all jobs or failed jobs only
github_cancel_workflow Cancel a workflow that is queued or in progress

Artifacts

Tool Description
github_download_artifacts List artifacts or get a pre-signed download URL for a specific one

Deployments

Tool Description
github_get_deployments List deployments by environment with latest status
github_rollback_deployment Create a new deployment pointing to a previous stable ref

Monitoring

Tool Description
github_monitor_repository Start / stop continuous polling of a repo for workflow changes

Environment Variables

Variable Required Default Description
GITHUB_TOKEN GitHub Personal Access Token (repo, workflow, read:org)
ANTHROPIC_API_KEY Anthropic API key for Claude-powered failure analysis
POLLING_INTERVAL_MS 30000 How often monitor_repository polls GitHub (milliseconds)
LOG_LEVEL info Log verbosity: debug / info / warn / error

Docker

# Start with Docker Compose (reads from .env automatically)
docker-compose up -d

# Or build and run manually
docker build -t github-actions-mcp .
docker run --env-file .env github-actions-mcp

Development

npm run dev        # Run with tsx (no build step needed)
npm run build      # Compile TypeScript to dist/
npm start          # Run compiled output
npm test           # Run all tests (Vitest)
npm run typecheck  # TypeScript type check without emitting

Running Tests

npm test
✓ tests/modules/logs-analyzer.test.ts   (5 tests)
✓ tests/modules/ai-engine.test.ts       (2 tests)
✓ tests/tools/repositories.test.ts      (2 tests)
✓ tests/tools/workflows.test.ts         (3 tests)

Test Files  4 passed (4)
Tests       12 passed (12)

Architecture

src/
├── index.ts                          # FastMCP server — wires all 13 tools
├── config/
│   └── env.ts                        # Zod-validated environment config
├── modules/
│   ├── auth/
│   │   └── github-auth.ts            # GitHub PAT → Octokit instance
│   ├── connector/
│   │   └── github-rest.ts            # All GitHub REST API calls (Octokit)
│   ├── logs/
│   │   └── logs-analyzer.ts          # Log parsing & error pattern detection
│   ├── ai/
│   │   └── ai-engine.ts              # Claude API — failure diagnosis
│   └── monitoring/
│       └── workflow-monitor.ts       # Polling-based repo watcher
└── tools/
    ├── repositories.ts               # github_list_repositories
    ├── workflows.ts                  # github_list_workflows, list_runs, get_run
    ├── watcher.ts                    # github_watch_workflow
    ├── logs.ts                       # github_get_workflow_logs, analyze_failure
    ├── actions.ts                    # github_rerun_workflow, cancel_workflow
    ├── artifacts.ts                  # github_download_artifacts
    ├── deployments.ts                # github_get_deployments, rollback_deployment
    └── monitor.ts                    # github_monitor_repository

How github_analyze_failure works

Agent calls github_analyze_failure(owner, repo, runId)
        │
        ├─► getWorkflowRun()        → confirm conclusion = "failure"
        ├─► getWorkflowRunJobs()    → identify failed jobs
        ├─► getJobLogs() × N        → fetch raw log text
        │
        ├─► LogsAnalyzer.parseWorkflowLogs()     → extract failed steps + errors
        ├─► LogsAnalyzer.extractErrorContext()   → get surrounding log lines
        │
        └─► AIEngine.analyzeFailure()            → send to Claude
                │
                └─► Returns: probableCause · suggestedFixes · severity · diagnosticReport

Roadmap

  • [ ] Web dashboard for real-time pipeline visualization
  • [ ] Slack / webhook notifications on workflow events
  • [ ] GitLab CI/CD support
  • [ ] Azure DevOps connector
  • [ ] Self-healing pipelines (auto-rerun with AI-suggested fixes)
  • [ ] Multi-repository aggregated dashboard

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you'd like to change.

  1. Fork the repository
  2. Create your feature branch (git checkout -b feat/amazing-feature)
  3. Commit your changes (git commit -m 'feat: add amazing feature')
  4. Push to the branch (git push origin feat/amazing-feature)
  5. Open a Pull Request

License

MIT — see LICENSE for details.


<p align="center"> Built with <a href="https://github.com/punkpeye/fastmcp">FastMCP</a> · Powered by muhammed ehab for github community </p>

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