Aidc Mcp Server
Size and validate AI datacenter (AIDC) designs—OPR/BOD and rackPlan layout—from a shared 3D graph before committing to physical build-out.
Overview
AIDC MCP Server is a MCP server for the Validate phase that sizes OPR/BOD and validates rackPlan layouts from a shared 3D AIDC graph.
What is this MCP server?
- npm stdio package aidc-mcp-server (registry 0.2.1, server schema 0.2.1-1)
- OPR and BOD sizing workflows tied to a shared 3D facility graph
- rackPlan layout generation and validation from the same graph model
- Published from aidc-ai-io monorepo subfolder mcp-server on GitHub
- Bridges AIDC domain tools to agents via Model Context Protocol at aidc-ai.io
- MCP server schema version 0.2.1-1
- npm package aidc-mcp-server at version 0.2.1 with stdio transport
- Source repository subfolder mcp-server under github.com/aidc2026ai-melon/aidc-ai-io
What problem does it solve?
AI datacenter layouts are hard to iterate in chat because OPR, BOD, and rack plans live in a 3D graph that agents cannot see without a dedicated MCP bridge.
Who is it for?
Builders and infra consultants modeling AI datacenter racks and power in the AIDC 3D graph who want agent-assisted sizing before build-out.
Skip if: Typical indie SaaS apps, generic AWS console workflows, or teams without the AIDC graph toolchain.
What do I get? / Deliverables
After install, your agent can invoke sizing and validation tools on the shared graph so scope decisions are backed by structured rack and capacity checks.
- Agent-driven OPR/BOD sizing against the 3D graph
- Validated rackPlan layout outputs for scope reviews
- Repeatable MCP tool surface for datacenter design iterations
Recommended MCP Servers
Journey fit
Datacenter capacity and layout decisions belong in validate when you are scoping whether a design meets power, space, and rack constraints before heavy implementation. Scope is the right subphase because OPR/BOD sizing and rackPlan validation are upfront planning artifacts, not day-two production monitoring.
How it compares
Specialized facility-design MCP for AIDC graphs, not a general cloud provisioning or Terraform skill.
Common Questions / FAQ
Who is AIDC MCP Server for?
It is for professionals planning AI datacenter capacity and rack layouts who already work in the AIDC shared 3D graph and use MCP-enabled agents.
When should I use AIDC MCP Server?
Use it during validate and scope when you need OPR/BOD sizing, design validation, or rackPlan layout checks before locking procurement or construction.
How do I add AIDC MCP Server to my agent?
Install the npm package aidc-mcp-server, configure stdio MCP in Claude Code or Cursor per the aidc-ai-io repo mcp-server docs, and ensure your 3D graph data is available to the server runtime.