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Pavan Kumar Sadashiv
Pavan Kumar Sadashiv

Posted on Originally published at dev.to

Prompting on a MacBook Air, Executing on Windows: Building a Remote MCP Infrastructure for Cadence Allegro X

#ai #automation #devtools #architecture

Prompting on a MacBook Air, Executing on Windows: Building a Remote MCP Infrastructure for Cadence Allegro X

When building complex multi-layer PCBs, heavy hardware design suites like Cadence Allegro X present a massive resource hurdle. The official platform guidelines call for a minimum of 50 GB of free disk space and up to 64 GB of RAM for intense routing jobs.

If your daily driver is a compact setup—like a MacBook Air M2 with a tight 256 GB drive—local virtualization (e.g., via Parallels Desktop) isn't practical. Splitting your 16 GB of RAM causes both macOS and the guest Windows instance to choke, and the 120+ GB virtual storage allocation leaves your local SSD running near maximum capacity.

To solve this, you can decouple your orchestration layer from your execution layer. By running a remote AI agent setup using the Model Context Protocol (MCP) and Google Antigravity, you can use a MacBook Air for context prompting while running the physical workloads on an external high-spec Windows machine (like an Infinix GT Book with a Core i9, 32 GB RAM, and a discrete RTX GPU).

Here is how to set up a cross-platform, network-isolated engineering pipeline that moves desktop automation across operating systems.

The Architecture Topology

Instead of launching local child processes over standard input/output (stdio), this distributed pattern relies on a Remote HTTP server configuration using Server-Sent Events (SSE). The MacBook Air serves as the human-in-the-loop validation client, and the Windows machine operates as the dedicated execution worker node.

+───────────────────────────────+               +───────────────────────────────────+
│   MacBook Air M2 (Client)     │               │    Windows Workstation (Node)     │
├───────────────────────────────┤               ├───────────────────────────────────┤
│  [Google Antigravity CLI]     │  Remote HTTP  │   [winremote-mcp Server]          │
│              │                │  (SSE/JSON)   │                 │                 │
│              ▼                ├──────────────►│                 ▼                 │
│  Sends Natural Language Prompts│               │   Executes Shell Commands         │
│  "Route this PCB layout..."   │               │   Controls Cadence Allegro X CLI  │
+───────────────────────────────+               +───────────────────────────────────+
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Step-by-Step Implementation Guide

1. Prepare the Windows Worker Node

First, configure the automation target environment to receive structured JSON-RPC calls from the orchestration layer.

Open a PowerShell terminal on the Windows machine and install the remote coordination bridge:

pip install winremote-mcp
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Next, boot the server and bind it to your local network interface. Make sure to note your local IPv4 address (e.g., 192.168.1.50):

winremote-mcp --host 0.0.0.0 --port 8090
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2. Bind the Client Layer on macOS

With the worker node listening, link the Google Antigravity runtime or active IDE context to the remote endpoint.

Locate your agent ecosystem definition layout (typically mcp-config.json or agent.json) on your MacBook Air and register the external server block:

{
  "mcpServers": {
    "infinix-windows-worker": {
      "url": "http://192.168.1.50:8090/sse",
      "type": "remote"
    }
  }
}
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Prompt Execution Mechanics

Once connected, your hardware workflow transitions into automated prompt loops:

  1. Context Submission: Submit a structural design prompt (e.g., "Import the footprints from library X and run the automated placement script on layer 3") directly into the Antigravity prompt interface on the Mac.
  2. Step Calculation: The reasoning engine parses the design request and translates it into a deterministic order of operations.
  3. Network Delegation: Antigravity routes these instructions as standardized tool-call payloads across the local network via HTTP SSE.
  4. Native Execution: The Windows handler receives the JSON payloads, maps them to local shell tools or Cadence CLI executables, and utilizes the system's Core i9 and RTX GPU pipelines to run the tasks.

Critical Production Realities

  • Directory Mapping: When writing prompt paths, ensure you reference files using Windows directory layouts (e.g., C:\Cadence\Projects\...) rather than standard macOS POSIX routes.
  • Network Isolation: Basic remote MCP utilities typically lack robust built-in authentication out of the box. Keep this entire setup isolated behind a trusted local router or encrypted VPN link to eliminate Remote Code Execution risks.

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