# dxpert.ai — industrial AI-readiness & UNS agents

Brings industrial AI readiness diagnostics, UNS architecture design, and specialized plant agents to your local assistant via the dxpert API.

- MCP server · `type=mcp` · Claude Code, Codex, Cursor, GitHub Copilot, Windsurf (Devin Desktop), Cline, Zed, Gemini CLI
- Author: dxpert-ai
- License: MIT · policy=link-only · description only; the file stays with the author
- Source: https://github.com/dxpert-ai/dxpert-mcp
- Updated: 2026-10-05
- HTML: https://affpapa.org/en/nevibecode/mcp/dxpertai-industrial-ai-readiness-uns-agents

## What it does

This stdio MCP server connects your development environment to the dxpert.ai API. It adds tools for industrial domain guidance, including `ask_dxpert` for questions about OT/IT data architecture and Unified Namespace layout. You can also run readiness assessments via `run_diagnostic` or trigger specialized factory agents for shift summaries, root cause investigation, alarm triage, and OEE narration.

The package operates without third-party runtime dependencies. It handles the JSON-RPC stdio protocol directly and forwards requests to the cloud using the `X-Api-Key` header. Releases publish to npm via GitHub Actions OIDC trusted publishing. A built-in `csv_to_bundle` tool formats standard spreadsheet exports so agents can inspect them immediately.

Every request requires the `DXPERT_API_KEY` environment variable. Without this credential, the server returns an authorization error. The agents operate strictly on the bundle passed into the tool call: they do not connect to live plant networks, brokers, or historians. A free tier provides 10 advisory questions per month and 5 trial agent runs before requiring a Pro upgrade.

## Requirements
- Needs: Node.js (commands run via npx)
- Environment variables: `DXPERT_API_KEY`, `DXPERT_API_BASE` — keys and tokens: ask the human for the values, never commit them or put them in shared files.

## Install

### Claude Code (native platform)

**Platform CLI**
```bash
claude mcp add --env DXPERT_API_KEY=<DXPERT_API_KEY> --env DXPERT_API_BASE=<DXPERT_API_BASE> --transport stdio dxpertai-industrial-ai-readiness-uns-agents -- npx -y @dxpert/mcp
```
Replace <…> with your own values

### Codex

**Platform CLI**
```bash
codex mcp add dxpertai-industrial-ai-readiness-uns-agents --env DXPERT_API_KEY=<DXPERT_API_KEY> --env DXPERT_API_BASE=<DXPERT_API_BASE> -- npx -y @dxpert/mcp
```
Replace <…> with your own values

### Cursor

**Config file**
File `.cursor/mcp.json`:
```json
{
    "mcpServers": {
        "dxpertai-industrial-ai-readiness-uns-agents": {
            "command": "npx",
            "args": [
                "-y",
                "@dxpert/mcp"
            ],
            "env": {
                "DXPERT_API_KEY": "<DXPERT_API_KEY>",
                "DXPERT_API_BASE": "<DXPERT_API_BASE>"
            }
        }
    }
}
```
Replace <…> with your own values

### GitHub Copilot

**Config file**
File `.vscode/mcp.json`:
```json
{
    "servers": {
        "dxpertai-industrial-ai-readiness-uns-agents": {
            "type": "stdio",
            "command": "npx",
            "args": [
                "-y",
                "@dxpert/mcp"
            ],
            "env": {
                "DXPERT_API_KEY": "<DXPERT_API_KEY>",
                "DXPERT_API_BASE": "<DXPERT_API_BASE>"
            }
        }
    }
}
```
Replace <…> with your own values

### Gemini CLI

**Platform CLI**
```bash
gemini mcp add -e DXPERT_API_KEY=<DXPERT_API_KEY> -e DXPERT_API_BASE=<DXPERT_API_BASE> dxpertai-industrial-ai-readiness-uns-agents npx -y @dxpert/mcp
```
Replace <…> with your own values

## When to use it
- evaluate plant IT infrastructure for industrial AI readiness
- design Unified Namespace hierarchies and OT/IT topic structures
- generate plant shift reports directly from spreadsheet exports
- investigate root causes of machinery downtime using incident context
- prioritize and triage high-volume industrial alarm streams

## Example prompts
- Ask dxpert how to structure our Unified Namespace topic hierarchy for packaging lines.
- Convert `shift_log.csv` into a payload using `csv_to_bundle` and generate a shift report.
- Run the dxpert diagnostic intake to assess our plant readiness for machine learning.

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