Python

A language model is a function from text to text. You send it a list of messages, it returns one message, and nothing else happens. It cannot read your disk, check the clock, remember your previous question, or run the tool it just asked for. Every capability an AI product appears to have belongs to the program wrapped around the model. That program is the harness: the code that builds each request, executes the tools the model asks for, and decides what the model is shown in the first place. ...

An MCP server that adds two numbers has no opinion about who is calling it. One that posts to a social account has to answer a question before it can do anything at all: whose account? There are two workable answers. You can build a multi-tenant server that authenticates every caller and looks up the account they own, which is the shape Add GitHub OAuth to a FastMCP Server builds. Or you can build one process that acts as exactly one account, reads that account out of its own environment, and runs on the same machine as the client calling it. ...

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. ...

Hand-written JSON fixtures cover the two or three cases you thought of while writing them. A generator covers hundreds, and it keeps covering them after the schema changes. This article builds two generators for the same order-intake API: a naive one that invents every field from random primitives, and a second one that samples a JSON seed database so every request refers to a product and a customer the API actually knows about. ...

uvx ruff check . works without installing anything. That is the whole appeal: one command, a throwaway environment, nothing left behind. It is also the problem. Every invocation re-resolves the package, the version you get today is not necessarily the version you got last week, and which ruff comes back empty because there is nothing on PATH. uv tool install is the other half of the story. It builds a persistent, isolated environment for a Python CLI and links its executables into a directory on your PATH, so ruff becomes a real command with a version you chose and control. Homebrew’s pipx niche, in other words, handled by the tool you already have. ...

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. ...

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: ...

A sibling article, Publish a FastMCP Server to PyPI and Run It Anywhere with uvx, ships a package to the public PyPI index. This one skips the index entirely. If your code is a Git repository, uvx can install and run its console script straight from the repo: uvx --from git+https://github.com/your-username/textkit textkit --help That command clones the repo into a cached, throwaway environment, builds the package, and runs its console script — no git clone, no virtualenv, no pip install on the user’s side. For a personal utility, an internal tool, or anything you are not ready to name on PyPI, a GitHub repo is the whole distribution channel. ...