# dot-ai [Health: Active]

**Category:** ☁️ Cloud Platforms  
**Repository:** https://github.com/vfarcic/dot-ai  
**GitHub Stars:** 339  
**npm Downloads (last month):** 1375  
**Views:** 0  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/dot-ai

## Description
AI-powered development platform for Kubernetes deployments and intelligent automation

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `npx` (confidence: high):

```json
"mcpServers": {
  "dot-ai": {
    "command": "npx",
    "args": ["-y","@vfarcic/dot-ai"]
  }
}
```

## Documentation

## What dot-ai does

The dot-ai MCP server connects AI-assisted development workflows with Kubernetes operations and platform-engineering tasks. It is designed for teams that want to use conversational requests to inspect cluster state, reason about deployments, investigate operational problems, and apply organization-specific guidance.

The project exposes its capabilities through MCP for AI coding assistants and also provides a CLI for direct agent integration. The MCP interface is the relevant option when an assistant needs access to the toolkit as part of a coding or operations conversation.

Supported use cases include querying and exploring clusters in natural language, receiving Kubernetes deployment recommendations, and analyzing issues for possible remediation and root causes. The toolkit also covers organizational patterns and policies, documentation search, repository initialization, and shared prompt management.

## How it works

An MCP-compatible assistant communicates with the toolkit and can request information or assistance related to Kubernetes and development workflows. Cluster output is handled separately from operator instructions during remediation and Day 2 operations through an untrusted-content boundary. This separation is intended to prevent data returned from the cluster from being treated as authoritative instructions.

The project is built around Kubernetes for cloud-native operations and uses the Model Context Protocol as its AI integration framework. Its broader stack also includes the Vercel AI SDK, which provides a common interface for AI providers.

The dot-ai MCP server can be used as an individual component or as part of the complete dot-ai stack. The project documentation recommends the full stack for the simplest setup because its components are preconfigured.

## Setup and configuration

The repository provides separate documentation for MCP setup, individual component deployment, and complete stack installation.

The project collects anonymous usage analytics. Its telemetry documentation describes how usage data is handled and how to opt out. Review that information before deploying it in an environment with strict telemetry requirements.

## Tools and capabilities

The dot-ai MCP server supports these capability areas:

- Natural-language Kubernetes cluster queries and exploration
- Kubernetes deployment recommendations
- Issue remediation and root-cause analysis
- Organizational pattern and policy management
- Semantic search across organizational documentation
- Automated repository setup with governance files
- Shared prompt libraries for consistent workflows

These capabilities make the server relevant to both cluster operations and development processes that depend on organizational standards.

## Limitations and notes

The project is marked beta in the repository material, so teams should evaluate it against their operational requirements before relying on it for production workflows.

The toolkit is focused on Kubernetes, platform engineering, and related development workflows. It should be evaluated carefully if the intended use case requires a different infrastructure platform or integrations that are not described in the project material. The repository is licensed under MIT.

_Full upstream README: https://allmcps.com/mcp/dot-ai/readme_