servers VS ollama

Most agents don't speak raw HTTP — they speak MCP. So the real distribution surface is a tiny MCP server that exposes each endpoint as a tool and does the paying under the hood. The agent calls web_search(query); the server hits the paid endpoint, handles the 402 with its operator's wallet, returns JSON. One line to install:

The Model Context Protocol (MCP) is an open standard for connecting AI hosts (Claude, ChatGPT, Cursor, VS Code, and others) to external context and actions through a structured protocol instead of ad-hoc plugins.

Model Context Protocol specification

That changed when I built mpesa-mcp — a Model Context Protocol server that wraps the Daraja API and makes it directly available to Claude, GPT-4, and Gemini.

If you haven't run into it yet, MCP (Model Context Protocol) is an open standard for exposing tools to AI agents. two-go ships an MCP server that runs over stdio with no dependencies, no URL, no account, and no API key. It's all local.

MCP server surface. The CLI is already agent-friendly, so I'm double minded about whether a Model Context Protocol server would make integration smoother. I'm personally not a fan of MCP and prefer skills and CLIs.

As noted in the Model Context Protocol (MCP) Introduction, this protocol allows Claude to interact with systems like GitHub and local databases through a standardized interface.

Keep the project's living context: current task state, decisions, conventions, known footguns as plain Markdown committed to the repo, and expose it to the agent over MCP.

The MCP ecosystem doubles every few months. Bookmark the official MCP servers repository for the canonical list.

The thing is, the AI isn't the hard part anymore. Anyone can run a capable model locally with Ollama in two commands, for free. What's missing is the interface. The reason Grammarly was worth paying for was never the grammar engine — it was the friendly diff that lets you see and control each change.

If you want GLM-5.2 running in under five minutes, Ollama is the path. It wraps llama.cpp in a managed runtime with one-command model pulls.

Software: Python 3.10+, Ollama installed

Ollama is probably the easiest starting point for local models. It gives developers a simple way to run and manage open models locally, and it exposes a REST API for chat and generation on localhost:11434. (Ollama) (GitHub)

Download: Go to https://ollama.com/ and click on the download link for your operating system.

Most AI resume tools call out to OpenAI or Anthropic and charge you for every request. Persona supports Ollama — which means you can run the AI model locally on your own hardware, with zero API costs and zero data leaving your machine.

To solve these, we built a stack that prioritizes integrity over ease. The centerpiece is Ollama, running the mxbai-embed-large model locally. This is the engine that translates human thought into high-dimensional coordinates.

Ollama for model serving

Fully offline: Point it at Ollama or LM Studio. Zero cost, nothing leaves your network.

Recommended hardware: The RTX 3060 with 12 GB VRAM is the budget king here — all these models fit with room to spare for KV cache overhead, even Gemma 4:12B (which needs ~8.5–9 GB with overhead). An RTX 4060 Ti 16 GB gives you more headroom. On the Apple side, any M2 or M3 MacBook with 16 GB unified memory handles these models comfortably via Ollama's Metal backend.