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Hermes Agent installs from a shell script in a couple of minutes. Installing it the same way twice, a year apart, on a machine you have since forgotten the details of, is the harder problem — and it is the one Ansible solves. This article builds one role that installs Hermes on two Macs that differ in the ways that actually matter: devbot5 — the Mac you are typing on minime — a headless Mac mini Connection local, no SSH at all ssh Runs as your login account a dedicated service account launchd job LaunchAgent in your home LaunchDaemon in /Library API bound to 127.0.0.1 0.0.0.0 Those last two rows are what this article is about. A headless Mac has no one logged in, so there is no GUI session for a LaunchAgent to live in and the job has to be a LaunchDaemon that starts at boot and drops privileges. A laptop joins hotel and coffee-shop networks, so binding an agent’s API to every interface there would publish a shell to whoever else is on that LAN. ...

Hermes Agent is Nous Research’s open-source (MIT) agent runtime: one agent with persistent memory that you can reach from a terminal or from a chat platform, backed by whichever model provider you point it at. Unlike an agent library, it ships as an installed program with its own config directory, a messaging gateway, and a command approval layer, so most of the work in getting started is deciding what it is allowed to do rather than writing code. ...

Add Per-Plan Rate Limiting to a FastMCP Server on macOS closes its troubleshooting with “back it with Redis (shared, atomic counters) if you run several instances behind a load balancer”, and Add Observability to a FastMCP Server on macOS adds a /health endpoint “for load balancers”. Neither article puts a load balancer in front of anything. This one does, and the first thing that happens is that the server stops working. ...

Generate Synthetic JSON Requests to Test an API on macOS built two generators and a CLI that fires batches at an order-intake API. This article puts the same generators behind an MCP server, so an agent can preview a payload, check the target, and trigger a batch by asking for one. Wrapping a generator in tools is the easy half. The half worth attention is the control surface: which decisions the caller gets to make and which ones the server keeps. A model that can pick the destination of a traffic generator is a server-side request forgery primitive with a friendly name, and a model that can pick the batch size can turn one sentence into fifty thousand POSTs. Here the target comes from the environment and the batch size is capped, so the tools stay useful without handing over either decision. ...

Most teams have a procedure that only lives in someone’s head. Cutting release notes, triaging a breaking change, prepping an on-call handoff: five steps, done slightly differently every time, and badly the week the person who knows them is on vacation. MCP gives you three primitives to fix that, and the interesting one is the least used. Tools are called by the model. Resources are read for context. Prompts are chosen by a person: named, parameterized templates a client surfaces as a slash command. That makes a prompt the natural home for a procedure — the user picks it, fills in one argument, and the model runs the same five steps in the same order every time. ...

A resource that returns a picture is the most demanding shape an MCP resource takes. It has to survive base64 encoding, arrive with a MIME type the client will accept, stay small enough to attach, and — if the picture is meant to reflect something live — return different bytes the next time the same URI is read. Serve Resources Well from an MCP Server on macOS covered the shapes of resources with an eight-byte PNG magic number standing in for a binary body. This article replaces that stub with a real image: a bar chart rendered on every read from state that anything can change, packaged so uvx runs it from a local directory, and wired into opencode so a model can read it, watch it change, and read it again. ...

opencode attaches an MCP server in a single command, with no config file to hand-edit and no JSON to get wrong. Everything after the -- separator is the command that launches the server, and because uvx resolves the package on first launch, there is nothing to install beforehand either: opencode mcp add hello -- uvx mcp-hello-server This tutorial uses that command to attach mcp-hello-server — a small FastMCP server on PyPI exposing two tools, server_info and greet — then drives it from a free model. You will confirm the server connects, prove the launch command works without involving a model at all, pin the same server to a single project instead of your whole machine, and finally ask North Mini Code Free to say hello to Alice in Japanese. ...

The read-only Postgres MCP server built its safety on the database server: a SELECT-only role, a read-only transaction, a statement timeout, all enforced by a process on the other side of a socket. SQLite has none of that machinery. There is no server, no accounts, no GRANT. The database is a file, the engine runs inside your process, and any connection that can open the file read-write can do anything to it. ...

Give a language model a database tool and the obvious failure mode is not a clever exploit. It is a well-meaning model running DELETE FROM orders because a prompt told it to “clean up test data,” or issuing SELECT * FROM events against a billion-row table and stalling everything behind it. A database MCP server has to assume the caller is careless, and sometimes hostile. In this tutorial you will build a FastMCP server that exposes a Postgres database to an MCP client as strictly read-only, with the safety built in layers so no single mistake opens a hole: ...

Packaging an MCP server as a Docker image gives clients one launch command with no Python, no uv, and no virtual environment to manage on the host: they run the container. This tutorial builds a small, non-root image for a FastMCP server with a multi-stage build, runs it over stdio the way a client does, and wires it into a client’s .mcp.json with docker run. The build uses uv in the builder stage and copies only the finished virtual environment into a slim runtime stage, so the final image carries the app and its dependencies — not the build toolchain. ...