Connecting AI Agents to Your Homelab: Building a Multi-Server MCP Architecture with Antigravity

Wed, 07 Oct 2026 10:00:00 +0200

Autonomous AI coding agents (like Antigravity, Claude Code, and Cursor) are remarkable at writing code, but by default they are completely blind to your actual homelab infrastructure. If you ask an agent “Why is container X restarting?” or “Check if port 8080 is open on my UniFi switch”, it has no way to see your live environment without manual copy-pasting.

The Model Context Protocol (MCP), open-sourced by Anthropic, changes this fundamentally. In this post—the first in our Homelab MCP series—I show how I built a multi-server MCP mesh connecting Antigravity to our UniFi network, Unraid storage server, Home Assistant, and Zero Trust gateways.

What is MCP and Why Does Homelab Need It?

MCP is an open standard that lets LLMs and AI agents discover tools, read resources, and execute actions across external services via standardized JSON-RPC protocols.

Instead of writing custom API wrappers for every script, MCP standardizes two transport modes:

Stdio (Standard I/O): Local processes spawned directly on your workstation (e.g. npx @google-cloud/gcloud-mcp).
HTTP / SSE (Server-Sent Events): Remote containerized microservices listening on your homelab network (e.g. FastMCP containers on Docker).

+-------------------------------------------------------------+
|                 Antigravity CLI / Desktop                   |
|                   (~/.gemini/antigravity)                   |
+------------------------------+------------------------------+
                               | (JSON-RPC over HTTP)
        +----------------------+----------------------+
        |                      |                      |
        v                      v                      v
+---------------+      +---------------+      +---------------+
|   mcp-unifi   |      |  mcp-unraid   |      |    mcp-ha     |
| (192.168.250) |      | (192.168.255) |      | (192.168.192) |
+---------------+      +---------------+      +---------------+

Central MCP Configuration: `mcp_config.json`

In Antigravity, all active MCP tools are registered in a single central manifest (~/.gemini/antigravity/mcp_config.json):

{
  "mcpServers": {
    "mcp-unifi": {
      "type": "http",
      "url": "http://192.168.250.2:32000/mcp",
      "httpUrl": "http://192.168.250.2:32000/mcp"
    },
    "mcp-unraid": {
      "type": "http",
      "url": "http://192.168.255.50:8442/mcp",
      "serverUrl": "http://192.168.255.50:8442/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_UNRAID_API_KEY"
      }
    },
    "mcp-homeassistant": {
      "type": "http",
      "url": "http://192.168.192.168:8123/api/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_HA_LONG_LIVED_TOKEN"
      }
    },
    "mcp-pangolin": {
      "type": "http",
      "url": "http://192.168.250.2:32001/mcp"
    },
    "mcp-obsidian": {
      "type": "http",
      "url": "https://127.0.0.1:27124/mcp/",
      "headers": {
        "Authorization": "Bearer YOUR_OBSIDIAN_KEY"
      }
    }
  }
}

How it Works in Practice

Once loaded, Antigravity lazily indexes dozens of specialized tools:

mcp_unifi_execute: Queries real-time client RSSI, VLAN port profiles, and PoE budgets.
mcp_unraid_get_system_info: Inspects array parity status, ZFS pool health, disk SMART temperatures, and CPU load average.
mcp_ha_get_state: Reads room temperatures, vehicle battery levels, and energy forecasts.

Now, instead of manually checking four web dashboards, I can ask Antigravity:

“Check if any homelab host is under heavy load and confirm if all UniFi APs are operating normally.”

The agent calls mcp-unraid and mcp-unifi concurrently, aggregates the JSON responses, and delivers an instant executive summary.

In the upcoming posts of this series, we will break down each server implementation with complete Docker Compose configs and Python FastMCP code!

Private & Self-Hosted AI Web Search: Integrating SearXNG with Open WebUI for Local RAG

Wed, 30 Sep 2026 10:00:00 +0200

Enabling real-time web browsing in local AI chat interfaces transforms large language models from static knowledge bases into dynamic research assistants. However, relying on default search providers like DuckDuckGo frequently breaks when running in containerized homelabs or cloud datacenters.

In this guide, I explain why datacenter search gets blocked and how to deploy a private SearXNG instance to provide reliable, rate-limit-free search grounding for Open WebUI.

The Problem with Datacenter Web Search

When Open WebUI queries search engines via DuckDuckGo:

DuckDuckGo aggressively rate-limits or outright blacklists IP ranges belonging to hosting providers, VPSs, and residential VPN endpoints (e.g., Cloudflare tunnels or VPS proxies).
The user sees generic RAG search error: 403 Forbidden or empty context cards.

The Solution: Self-Hosted SearXNG Meta-Search Engine

SearXNG is a privacy-respecting, hackable metasearch engine that aggregates results from more than 70 search services (Google, Bing, Wikipedia, DuckDuckGo, Reddit, GitHub, etc.) while obfuscating client requests.

1. Docker Compose Deployment

services:
  searxng:
    image: searxng/searxng:latest
    container_name: llm-searxng
    restart: unless-stopped
    environment:
      - SEARXNG_BASE_URL=http://llm-searxng:8080/
      - UWSGI_WORKERS=4
      - UWSGI_THREADS=4
    volumes:
      - /mnt/appdata/searxng:/etc/searxng
    networks:
      - frontend

Pro-Tip on Port Management: Avoid exposing SearXNG directly on host ports like 8080:8080 without checking for port collisions (ss -tulpn | grep 8080). In our homelab architecture, we keep SearXNG inside the internal Docker frontend bridge network, accessible only to Open WebUI!

2. Configuring Open WebUI

In Open WebUI:

Navigate to Admin Panel → Settings → Web Search.
Set Search Engine to searxng.

Set Searxng Query URL to:

`1`	`http://llm-searxng:8080/search?q=<query>`

Configure RAG extraction:
- RAG Top K: 5
- RAG Template: Include current timestamp and domain source citations.

Now, when you ask your model: “What happened in tech today?”, Open WebUI queries SearXNG across multiple search engines in parallel, extracts the raw HTML text, and feeds it directly into your model’s context window!

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