AIDC-AI.IO — MCP Connector

AI data center sizing, validation, and layout via a remote MCP server.
AI 데이터센터 자동화 툴: 결정론적 엔진으로 AI 데이터센터를 설계·검증·레이아웃합니다.

What is this?

This repository shows how to connect an MCP client or REST client to the AIDC-AI.IO Design Engine — a deterministic, source-backed engine that sizes, validates, and lays out Rubin-era AI data centers.

What the engine does (on the server):

• Accepts an IT load, rack density, GPU generation (Hopper / Blackwell / NVIDIA Vera Rubin NVL72 / VR200), and site constraints.
• Returns deployment-unit-snapped rack counts, design PUE, power-factor-backed total MVA (22.9 kV intake), liquid-cooling / air-cooling heat split, CDU planning values, cost (KRW), and timeline.
• Validates designs against electrical, cooling, layout, safety, and data rules with severity-classified findings and RFIs.
• Generates a rack-plan grid (hall dimensions, row/column positions in mm) and a site-block layout.

What this repo contains:

• MCP client configuration snippet.
• curl and Node.js examples that call the public REST projection (/api/agent/*).
• An illustrative response so you know what fields to expect.

The core calculation engine, reference catalogs (rack library, AHJ/code matrix, 1.6T fabric topology, direct-to-chip (D2C) cooling models, etc.) are proprietary and remain server-side. No engine source is published here.

Korea live. Region-specific: 22.9 kV utility intake, Korean AHJ/code, climate, and operations validation. Keywords the engine targets: AI data center, AIDC, NVIDIA Rubin, Vera Rubin, 22.9kV, liquid cooling, CDU, D2C, 1.6T fabric, PUE.

MCP Server

Tools

Quick Start

MCP client configuration

Add this to your MCP client config (e.g. Claude Desktop claude_desktop_config.json, Cursor MCP settings, or any Streamable HTTP client):

{
  "mcpServers": {
    "aidc-design-engine": {
      "url": "https://aidc-ai.io/api/mcp"
    }
  }
}

The server is immediately usable without an API key. To raise the rate limit, add:

{
  "mcpServers": {
    "aidc-design-engine": {
      "url": "https://aidc-ai.io/api/mcp",
      "headers": {
        "Authorization": "Bearer aidc_live_<your-32-hex-key>"
      }
    }
  }
}

Contact contact@aidc-ai.io for a registered or partner key.

Docker (local stdio server)

Build and run the same published MCP server used for registry evaluation:

docker build -t aidc-ai-mcp .
docker run --rm -i aidc-ai-mcp

The container communicates over stdio and connects to https://aidc-ai.io by default. No API key is required for the anonymous tier.

REST Usage

The MCP tools proxy to these REST endpoints (permissive CORS, same optional auth):

Example: size a 30 MW Rubin-era AI data center

curl -s -X POST https://aidc-ai.io/api/agent/design \
  -H "Content-Type: application/json" \
  -d '{
    "itLoadMw": 30,
    "rackDensityKw": 120,
    "gpuGen": "rubin",
    "siteAreaSqm": 5000,
    "region": "metropolitan",
    "options": {
      "redundancy": "n_plus_1",
      "coolingMode": "liquid",
      "pueTarget": 1.2
    }
  }'

Illustrative response

The JSON below is illustrative — field names and structure reflect the actual API shape, but exact numbers will vary by engine version and input. See design.response.example.json for the full object.

{
  "rackCount": 256,
  "rackCountRaw": 250,
  "pueDesign": 1.21,
  "mvaTotal": 45.8,
  "liquidCoolingLoadMw": 26.4,
  "airCoolingLoadMw": 3.6,
  "cduCount": 13,
  "totalCostKrw": 187500000000,
  "totalMonths": 28,
  "warnings": []
}

(30 MW IT / 120 kW per rack / Rubin / 5 000 m² / metropolitan / N+1 / liquid / PUE 1.2 target)

Tools — Input Reference

design

Size an AI data center from scratch.

Key response fields: rackCount, rackCountRaw, pueDesign, mvaTotal, liquidCoolingLoadMw, airCoolingLoadMw, cduCount, totalCostKrw, totalMonths, warnings[]

validate

Check a design against engineering rules.

{
  "rawInput": {
    "itLoadMw": 30,
    "rackDensityKw": 120,
    "gpuGen": "rubin",
    "siteAreaSqm": 5000,
    "region": "metropolitan"
  }
}

Key response fields: findings[] (each with severity, code, message), rfis[], passCount, warnCount, failCount

layout

Generate a rack plan and site block layout.

{
  "design": {
    "itLoadMw": 30,
    "rackDensityKw": 120,
    "gpuGen": "rubin",
    "siteAreaSqm": 5000,
    "region": "metropolitan"
  },
  "siteCentroid": { "lat": 37.5665, "lng": 126.9780 },
  "siteAreaSqm": 5000
}

Key response fields: rackPlan (hall dimensions, rows, columns, per-rack positions in mm), sitePlan (block-level layout in percentage coords)

Links

License

This repository (examples and connector code only) is released under the MIT License.
The AIDC-AI.IO engine, reference catalogs, and all server-side logic remain proprietary.