Aidc AI MCP

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.

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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/*).
• Authenticated runnable examples that read the current response shape.

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.

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MCP Server

Tools

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Quick Start

MCP client configuration

Obtain a registered integration key through AIDC Contact. Use a client that supports Streamable HTTP and pass the key through its credential settings as an Authorization: Bearer header. The mcp.json example contains a replacement marker, not a working credential. Keep the configured copy private and never commit a real key.

For a local stdio client, the published package is available through npm:

npx -y aidc-mcp-server@0.2.4

Set AIDC_API_KEY in the MCP server process's environment using your client's secret or environment configuration. A file next to the server is not read automatically. design, validate, and layout do not accept credentials as tool inputs. Authentication errors are connection-configuration failures, not engineering verdicts.

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 -e AIDC_API_KEY aidc-ai-mcp

The container communicates over stdio and connects to https://aidc-ai.io by default. Export a registered AIDC_API_KEY before running the container; -e AIDC_API_KEY forwards the existing variable without placing its value in the command text.

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REST Usage

The MCP package calls these REST endpoints. All calculation routes require a registered key:

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" \
  -H "Authorization: Bearer ${AIDC_API_KEY}" \
  -d '{
    "itLoadMw": 30,
    "rackDensityKw": 120,
    "gpuGen": "rubin",
    "siteAreaSqm": 5000,
    "region": "metropolitan",
    "options": {
      "redundancy": "n_plus_1",
      "coolingMode": "liquid",
      "pueTarget": 1.2
    }
  }'

Read the response

Use node design.example.js for a live authenticated example. Successful responses contain ok, summary, warnings, engineVersion, requestId, and _agent. Sizing values such as rackCount, pueDesign, and mvaTotal are inside summary. Preserve warnings and validation findings; an ok: true response does not certify the design or turn PENDING into PASS.

There is no fixed numeric output fixture for a real project. Use the returned engine result and its evidence for the exact selected inputs.

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Tools — Input Reference

design

Size an AI data center from scratch.

Key response fields: summary.rackCount, optional summary.unsnappedRackCount, summary.pueDesign, summary.mvaTotal, and cooling/commercial values inside summary. warnings[], engineVersion, and requestId are top-level fields.

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validate

Check a design against engineering rules.

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

Key response fields: findings[] (including severity, family, message, and optional publicRuleId), rfis[], and optional verdict / graphVerdict. Public MCP accepts rawInput or designSummary; private EngineSession IDs remain in the signed-in AIDC workflow.

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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)

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Links

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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.