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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. ...

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. ...

A JavaScript module is a file that exports values other files import by name. ES modules (import/export) are the standard form and run unchanged in modern browsers and Node.js. This tutorial builds a small module with a default export and a named factory function, imports it from another file, tests it with Node’s built-in runner, and loads it in the browser — the same module code in both environments, no bundler. ...

A random boolean — a coin flip that returns 1 or 0 about half the time — is the smallest building block for simulations and generative art. The function is one line; the useful part is confirming it lands on each value roughly equally and never returns anything else. This tutorial writes the function as an ES module, pins its rounding behavior with a Node test suite, prints a distribution, and paints the flips on a browser canvas. ...

A fair coin flip returns true half the time. A weighted coin flip returns true a chosen fraction of the time — 10%, 25%, whatever the simulation or generative-art piece needs. The function is one line built on Math.random(); the useful part is confirming the true rate tracks the weight and that the boundaries behave. This tutorial writes it as an ES module, pins the behavior with a Node test suite, prints a distribution at 10%, and paints the results on a browser canvas. ...

Picking a random element from an array comes up constantly: rolling dice, shuffling prompts, choosing a color. The whole job is one line of JavaScript, but the interesting part is proving it behaves — that the picks are spread across the array and never fall off either end. This tutorial builds that one-liner as an ES module, backs it with a Node test suite that pins down the boundary behavior, adds a distribution demo, and then draws the results on a browser canvas so you can see the spread. ...