DevOps

devopsaitoolkit.com

MCP server for the DevOps AI ToolKit that searches DevOps knowledge, prompts, and technical guides.

1 endpoint13 known toolsFirst detected October 14, 2026Last detected October 14, 2026

ENDPOINT 1

https://mcp.devopsaitoolkit.com/mcp

No auth detected

MCP server metadata

Name: devops-ai-toolkit
Version: 1.0.0

Capabilities

tools.listChangedresources.listChangedprompts.listChanged

Server instructions

DevOps AI ToolKit — production-focused DevOps knowledge, troubleshooting, validation, auditing, and incident-analysis tools. Advisory and analytical only: it never executes infrastructure commands. Deterministic validator/audit findings are separated from advisory analysis. Every knowledge result links to a canonical devopsaitoolkit.com URL — cite it.

Known tools 13

search_devops_knowledge

Search DevOps AI ToolKit knowledge (guides, error/troubleshooting content, and prompts).

Inferred read-only

search_prompts

Search the DevOps AI ToolKit prompt library.

Inferred read-only

get_prompt

Retrieve a complete prompt from the DevOps AI ToolKit prompt library by slug.

Inferred read-only

search_guides

Search DevOps AI ToolKit technical guides (cornerstone Core Guides + long-form guides).

Inferred read-only

get_guide

Retrieve an individual DevOps AI ToolKit guide by slug, as structured Markdown (not raw HTML).

Inferred read-only

search_error_solution

Search the DevOps AI ToolKit error & troubleshooting library.

Inferred read-only

validate_yaml

Validate YAML syntax and common structural problems using the DevOps AI ToolKit validator engine.

Inferred read-only

validate_kubernetes

Validate Kubernetes manifests for YAML validity plus production-readiness concerns (resources, security context, probes, latest tags, replicas).

Inferred read-only

validate_terraform

Validate Terraform/HCL for syntax and common production-readiness / safety issues using the existing validator.

Inferred read-only

validate_dockerfile

Validate a Dockerfile for common production/security problems (root, latest tags, secret exposure, healthcheck, USER, pinning).

Inferred read-only

audit_docker_production

Run the DevOps AI ToolKit Docker Production Readiness Auditor (deterministic ~50-rule engine) on a Dockerfile or Compose file.

Inferred read-only

diagnose_incident

Produce a safest-first, evidence-driven troubleshooting runbook from symptoms.

Inferred read-only

generate_prometheus_alert

Generate a PrometheusRule (deterministic templates) for common service/scenario pairs, validated as YAML.

Inferred read-only

CONNECT WITH APPROVAL

Client installation

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

[mcp_servers.devops-ai-toolkit]
url = "https://mcp.devopsaitoolkit.com/mcp"
enabled = true

Claude Code

.mcp.json

{
  "mcpServers": {
    "devops-ai-toolkit": {
      "type": "http",
      "url": "https://mcp.devopsaitoolkit.com/mcp"
    }
  }
}

Claude Desktop

Settings → Connectors → Add custom connector

Name: devops-ai-toolkit
Remote MCP URL: https://mcp.devopsaitoolkit.com/mcp

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

{
  "mcpServers": {
    "devops-ai-toolkit": {
      "url": "https://mcp.devopsaitoolkit.com/mcp"
    }
  }
}

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "devops-ai-toolkit": {
      "type": "http",
      "url": "https://mcp.devopsaitoolkit.com/mcp"
    }
  }
}

Generic MCP

Client-specific MCP configuration

{
  "name": "devops-ai-toolkit",
  "transport": "streamable-http",
  "url": "https://mcp.devopsaitoolkit.com/mcp"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

TRUST AND VERIFICATION EVIDENCE

Loading Trust v2 evidence…

Checking the associated registrable domain. The BuiltWith key remains server-side.

Indexed

Evidence is source-attributed and does not guarantee that a third-party server is safe. Risk labels are conservative metadata heuristics.