Hook
When I first tried to scale my personal automation stack, the bottleneck wasn’t the LLMs – it was getting data into the right place. OpenClaw’s Model Context Protocol (MCP) lets agents talk to external services, but out‑of‑the‑box you only have a handful of servers. I needed a dozen, each tuned for a specific workload (code‑completion, image generation, vector search, etc.). In this post I walk you through how I wired twelve MCP servers into my OpenClaw instance, turned them into reusable cron‑jobs, and finally got reliable, low‑latency responses for my daily “agent‑as‑a‑service” workflow.
1. Why Twelve Servers?
Specialisation: One server for Claude‑3.5, another for GPT‑4o‑mini, a third for local‑only LLaMA‑2. Each model has its own cost, latency, and context‑length constraints.
Redundancy: If the OpenAI API hiccups, a fallback local model keeps my automations alive.
