devops-mcp

Unified MCP server for DevOps engineers — query and manage Kubernetes, ArgoCD, Prometheus, and PagerDuty from any MCP-compatible AI agent.

What is this?

devops-mcp is an open source Model Context Protocol server that gives AI agents (Claude, etc.) real-time read and write access to your infrastructure stack — all from a single install.

Instead of copy-pasting kubectl output into a chat window, you can ask:

"Why is the payments deployment in CrashLoopBackOff?" "What changed in the last ArgoCD sync for the auth app?" "Show me the p99 latency for the API gateway over the last hour." "Who's on call right now and what incidents are open?" "Debug the payments service - what's wrong with it?"

...and get live answers, sourced directly from your cluster and tooling.

Providers included:

Related MCP server: LUMINO MCP Server

Quick start

Claude Desktop (stdio — recommended)

Add this to ~/.config/claude/claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "devops": {
      "command": "npx",
      "args": ["-y", "@notharshhaa/devops-mcp@latest"],
      "env": {
        "KUBECONFIG": "/home/you/.kube/config",
        "ARGOCD_SERVER": "https://argocd.company.com",
        "ARGOCD_TOKEN": "your-argocd-token",
        "PROMETHEUS_URL": "http://prometheus.monitoring:9090",
        "PAGERDUTY_TOKEN": "your-pd-api-token",
        "LOKI_URL": "http://loki.monitoring:3100",
        "LOKI_TOKEN": "your-loki-token"
      }
    }
  }
}

Restart Claude Desktop. The devops server will appear in the tools list.

Claude Code (CLI)

claude mcp add devops-mcp -e KUBECONFIG=$HOME/.kube/config \
  -e ARGOCD_SERVER=https://argocd.company.com \
  -e ARGOCD_TOKEN=... \
  -e PROMETHEUS_URL=http://prometheus:9090 \
  -e PAGERDUTY_TOKEN=... \
  -e LOKI_URL=http://loki.monitoring:3100 \
  -e LOKI_TOKEN=... \
  -- npx -y @notharshhaa/devops-mcp@latest

Local dev / test

npx @notharshhaa/devops-mcp
# or clone and run:
git clone https://github.com/NotHarshhaa/devops-mcp
cd devops-mcp
npm install
cp .env.example .env   # fill in your values
npm run dev

Configuration

All config is via environment variables. Only set the ones for providers you actually use — providers with missing config are silently skipped.

# ── Kubernetes ────────────────────────────────────────────────
KUBECONFIG=/home/user/.kube/config       # omit to use in-cluster service account
K8S_CONTEXT=my-prod-context              # optional: pin a specific context
K8S_ALLOWED_NAMESPACES=default,backend   # optional: restrict namespace access

# ── ArgoCD ───────────────────────────────────────────────────
ARGOCD_SERVER=https://argocd.company.com
ARGOCD_TOKEN=eyJhbGci...                 # argocd account generate-token

# ── Prometheus ───────────────────────────────────────────────
PROMETHEUS_URL=http://prometheus:9090
PROMETHEUS_BEARER_TOKEN=                 # optional: for authenticated Prometheus

# ── PagerDuty ────────────────────────────────────────────────
PAGERDUTY_TOKEN=your-api-v2-token

# ── Loki ───────────────────────────────────────────────────
LOKI_URL=http://loki.monitoring:3100
LOKI_TOKEN=your-loki-token

# ── Transport ────────────────────────────────────────────────
# For stdio mode (default): no transport config needed
# For SSE mode: set these env vars
PORT=3000                                # SSE mode only
MCP_AUTH_TOKEN=shared-secret            # Bearer token for SSE authentication

# ── Safety ───────────────────────────────────────────────────
DEVOPS_MCP_DRY_RUN=false                # true = block all mutations globally
DEVOPS_MCP_AUDIT_LOG=/var/log/devops-mcp-audit.jsonl

Tool reference

All tools follow a three-tier safety model:

• Read — safe, no side effects, no confirmation needed

• Mutate — defaults to dry_run: true; set dry_run: false to execute

• Destructive — requires confirm: true as an explicit parameter

Kubernetes (k8s__*)

ArgoCD (argo__*)

Prometheus (prom__*)

Example usage:

# Get a human-readable health summary
prom__summarize_service_health(service="payments", timeframeMinutes=30, sloThreshold=0.05)

What it outputs:

• Latency: "Latency increased: 120ms → 480ms (+300%)" or "Latency stable: 125ms"

• Error rate: "Error rate crossed SLO (5%): 7.2%" or "Error rate within SLO: 2.1%"

• Traffic: "Traffic dropped: 500 → 350 req/s (-30%)" or "Traffic spike detected (+150%)"

• Overall assessment: Summary of issues and positive indicators

Why this matters: Instead of raw PromQL numbers that require interpretation, this tool provides actionable insights that AI agents can use directly in responses, making monitoring data actually useful for incident investigation and communication.

Loki (logs__*)

Example usage:

# Get recent error logs
logs__get_recent_errors(service="payments", namespace="default", minutes=30, limit=50)

# Search logs with custom query
logs__search(query='{service="payments"} |= level="error"', limit=100)

Why this matters:

• Metrics tell what: Prometheus shows you that latency increased or error rate crossed SLO

• Logs tell why: Loki shows you the actual error messages, stack traces, and context around failures

• Complete debugging: Without logs, you can see that something is broken but not understand the root cause

Output format:

• Structured log entries with timestamp, message, service, namespace, and extracted log levels

• Error count summaries and filtering

• Raw LogQL results for detailed analysis

This makes incident investigation complete by combining the "what" (metrics) with the "why" (logs).

PagerDuty (pd__*)

pd__summarize_incident

Example usage:

# Get an auto-summary of an incident
pd__summarize_incident(id="ABC123")

What it outputs:

• What happened: Incident title, description, severity, urgency, status, creation time, and duration

• Affected services: Service name, ID, and current status

• Probable root cause: Analysis of trigger alerts and log entries to identify likely causes

• Current status: Current incident state, assignees, acknowledgements, and notes count

Output format:

{
  "what_happened": {
    "title": "API Gateway High Error Rate",
    "description": "5xx error rate exceeded 5% threshold",
    "severity": "high",
    "urgency": "high",
    "status": "acknowledged",
    "createdAt": "2025-01-15T10:30:00Z",
    "updatedAt": "2025-01-15T11:45:00Z",
    "duration": "1h 15m"
  },
  "affected_services": [
    {
      "id": "P123456",
      "name": "API Gateway",
      "status": "critical"
    }
  ],
  "probable_root_cause": "Triggered by: High 5xx error rate from API Gateway pods",
  "current_status": {
    "status": "acknowledged",
    "lastUpdated": "2025-01-15T11:45:00Z",
    "assignees": ["john.doe@company.com"],
    "acknowledgements": 2,
    "notes": 3
  }
}

Why this matters: Instead of manually piecing together incident details from multiple API calls, this tool provides a comprehensive, human-readable summary perfect for:

• Demos: Shows AI's ability to understand and summarize complex incident data

• Real-world use: Quickly understand incident impact without digging through raw data

• Communication: Share concise incident summaries with stakeholders

Helm (helm__*)

Requirements: Helm CLI binary must be available in PATH.

Example usage:

# List all releases in a namespace
helm__list_releases(namespace="production")

# Check what values a release is using
helm__get_values(name="api-gateway", all_values=true)

# Rollback after a bad deploy
helm__rollback(name="api-gateway", revision=5, dry_run=false)

Cross-Provider Debugging (devops__*)

devops__debug_service

Example usage:

# Debug a service across all providers
devops__debug_service(service="payments", namespace="default")

What it checks:

• Kubernetes: Pod status, restart counts, readiness, deployment health, recent events

• ArgoCD: Sync status, health status, Git diff detection, deployment history

• Prometheus: Error rate (5xx responses), latency (p95), firing alerts

• PagerDuty: Active incidents matching the service name

Output format:

• Human-readable diagnosis with emoji indicators (⚠️ warnings, ❌ errors)

• Per-provider status sections

• Summary highlighting critical issues

• Raw JSON data for detailed analysis

This is the most powerful tool for incident investigation - it gives you a complete picture of what's wrong with a service in seconds.

devops__explain_change

Example usage:

# Explain what changed in the last hour
devops__explain_change(service="payments", namespace="default", timeframeMinutes=60)

What it analyzes:

• ArgoCD: Deployment history within the timeframe, including revision, author, repo, and chart

• Kubernetes: Current rollout status, replica counts, image tags, and deployment readiness

• Prometheus: Error rate trends, latency patterns, and traffic spikes over the time window

Output format:

• Timeline of recent deployments with full metadata

• Kubernetes rollout status and health

• Metric anomaly detection (error rate spikes, latency issues, traffic changes)

• Correlation analysis that links deployments to metric changes

• Summary with root cause hypothesis

Problem it solves: "Everything was working yesterday… what changed?"

This tool answers that question by correlating deployment events with metric anomalies, helping you quickly identify whether a recent deployment, config change, or external factor caused the issue.

devops__runbook

Example usage:

# Diagnose a crashlooping service
devops__runbook(symptom="crashloop", service="payments", namespace="default")

# Investigate high latency
devops__runbook(symptom="high-latency", service="api-gateway")

Supported symptoms:

Output: Structured JSON with steps_executed[], findings[], and recommended_actions[].

devops__health_report

Example usage:

# Get a full cluster health assessment
devops__health_report(namespace="production")

What it gathers:

• Kubernetes: Unhealthy pods, deployments not at desired replicas

• Prometheus: Count of firing alerts

• ArgoCD: Out-of-sync and unhealthy applications

• PagerDuty: Open incident count

Output: Overall status (healthy / degraded / critical), per-provider sections, and summary. Perfect for morning standup checks or shift handoffs.

Deployment options

stdio (recommended for local use)

The MCP host launches devops-mcp as a subprocess and communicates over stdin/stdout. Zero network config. Auth comes from the local environment (kubeconfig, env vars). Process lifecycle tied to Claude Desktop.

npx @notharshhaa/devops-mcp
# or with env vars
KUBECONFIG=~/.kube/config npx @notharshhaa/devops-mcp

SSE / HTTP (for shared teams)

Server runs as a persistent HTTP service. Claude connects over Server-Sent Events. Enables multiple users sharing one server. Needs TLS + a bearer token or mTLS in front. Deploy via Docker on an internal bastion.

npx @notharshhaa/devops-mcp-sse
# or with env vars
PORT=3000 MCP_AUTH_TOKEN=your-secret npx @notharshhaa/devops-mcp-sse

For team use, put it behind a TLS-terminating reverse proxy (Caddy, nginx, Traefik). A minimal docker-compose.yml is in the examples/ directory.

WebSocket (optional extra)

Run @notharshhaa/devops-mcp with WebSocket transport for real-time bidirectional communication (not in reference implementation).

TRANSPORT=websocket PORT=3000 MCP_AUTH_TOKEN=your-secret npx @notharshhaa/devops-mcp

Connect to ws://localhost:3000/ws with the auth token in the Authorization header.

Security model

devops-mcp is designed for internal use inside a trusted network. That said:

• Kubernetes: Uses standard kubeconfig via @kubernetes/client-node. Supports exec plugins (AWS EKS, GKE). In-cluster: auto-mounts SA token. Add RBAC rules scoped to your desired permissions — run devops-mcp under a dedicated ServiceAccount with minimal verbs.

• ArgoCD: Generate a long-lived token: argocd account generate-token --account devops-mcp. Create a dedicated account in argocd-cm with apiKey capability and a role limited to read + sync.

• Prometheus: Usually unauthenticated inside a cluster. If using Grafana Mimir or Thanos with auth, pass a Bearer token. All tools are read-only so minimal permissions are needed.

• PagerDuty: Create a dedicated API key in PagerDuty → API Access → Create New API Key. Use Full Access if you want acknowledge/escalate tools; Read-only if you want a safe-only mode.

• Mutations are dry-run by default. Every mutating tool defaults dry_run: true. The AI must explicitly pass dry_run: false — it won't do this unless the user clearly requests an action.

• Destructive tools require confirm: true. This parameter is never passed by default; it requires the user to explicitly approve.

• Audit log. Set DEVOPS_MCP_AUDIT_LOG to a file path. Every tool call is written as a JSONL line with timestamp, tool name, parameters, and outcome. Mutations and destructive calls are flagged.

• Global dry-run mode. Set DEVOPS_MCP_DRY_RUN=true to prevent all mutations — useful for read-only team deployments.

Architecture

Client / UI agents (Claude Desktop, Claude Code, etc.)
       │
       ▼
  Transport Layer
  ┌──────────────────────────────┐
  │ stdio | SSE | WebSocket      │  ← Multiple transport support
  │ Authentication (token/JWT)    │  ← Dynamic auth system
  └──────────────────────────────┘
       │
       ▼
  Server & Auth/Registry
  ┌──────────────────────────────┐
  │ Tool registry & routing      │
  │ Dynamic auth manager         │  ← Session-based auth
  │ Request multiplexing         │  ← Concurrent request handling
  │ Audit logging                │
  └──────────────────────────────┘
       │
       ▼
  ┌────┬────┬────┐
  k8s  argo prom  pd  ← Provider modules
  │    │    │     │
  K8s  Argo Prom  PD  ← API clients
  API  API  HTTP  API
       │
       ▼
  Cross-cutting Concerns
  ┌──────────────────────────────┐
  │ Dry-run guard                │
  │ Audit logger                 │
  │ Error normalization          │
  │ Config loader                │
  └──────────────────────────────┘

Key architectural features:

• Multi-transport support: stdio and SSE transports using official MCP SDK

• Simple authentication: Bearer token for SSE transport (matches reference pattern)

• Provider isolation: Each provider (k8s, argo, prom, pd) is a self-contained module

• Cross-cutting concerns: Dry-run enforcement, audit logging, and error normalization applied consistently across all tools

Contributing

Contributions are welcome. The most useful areas:

• New providers — Grafana, Datadog, Vault, Terraform Cloud, Flux CD

• New tools — within existing providers (e.g. k8s__get_node_pressure, argo__get_app_logs)

• Better output formatting — richer structured responses for specific resource types

• Tests — unit tests for provider logic using mocked clients

Adding a new provider

1. Create src/providers/yourprovider/ with index.ts, client.ts, and one file per resource group.

2. Register it in src/server.ts.

3. Add config keys to .env.example and src/config.ts.

4. Document tools in this README following the existing table format.

5. Open a PR.

Local development

git clone https://github.com/NotHarshhaa/devops-mcp
cd devops-mcp
npm install
cp .env.example .env
npm run dev        # tsx watch — restarts on file change

Run against a local kind/minikube cluster for Kubernetes testing. Use DEVOPS_MCP_DRY_RUN=true to prevent accidental mutations during development.

Roadmap

• Grafana provider (grafana__*) — dashboards, annotations, datasources

• Flux CD provider (flux__*) — kustomizations, helm releases, image automation

• Terraform Cloud provider (tfc__*) — workspace runs, state, variables

• HashiCorp Vault provider (vault__*) — secret read (never write), lease status

• Datadog provider (dd__*) — metrics, monitors, events

• Web UI for SSE mode — connection status, live audit log, provider health

License

MIT — see LICENSE.

Built for DevOps and platform engineers who want AI that actually knows what's happening in their cluster.