Over the last few months I've built and shipped 100+ agents in public, many wired into MCP servers running against local models through Ollama, LM Studio, and decentralized open-source inference. A draw.io MCP server driven by Llama 3.2, an Excalidraw MCP integration, a Bright Data MCP briefing agent, all running end-to-end without a single token leaving the host.

This talk is a working engineer's tour of that stack. We'll walk through a minimal local MCP setup (server + client + Ollama) and then dig into the real-world failure modes: tool-selection collapse on 7B models, JSON-schema compliance gaps, capability-negotiation mismatches, and the surprisingly large quality delta between structured-output fine-tunes and general chat models. I'll share the prompt shapes, tool-description patterns, and schema-validation tricks that reliably push small open models from "demo-grade" to "I'd ship this internally."

Attendees will leave with a reference architecture for private MCP, a shortlist of open models that actually handle tool calls well today, and a set of design patterns for MCP servers that degrade gracefully when the client LLM has 8B parameters instead of a trillion.