servers VS zod

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 real fix is to check the data once, at the edge where it enters your app, and only hand the rest of your code values that have actually been verified. A schema validation library like Zod makes this the path of least resistance.

This post shows how to build a small todo MCP server with Node.js using the official @modelcontextprotocol/sdk package and Zod schemas.

This pattern works cleanly with Zod and React Hook Form. Zod schemas can infer TypeScript types directly, so you define validation rules and get the type for free:

Zod closes that gap by validating shape at runtime. The friction is hand-writing the schema. So I built a small in-browser tool that does it for you — paste a JSON sample, get a runnable z.object. This post is about the four non-obvious algorithm choices that came out of that build.

Libraries like React Hook Form, Formik, and Zod for schema validation make the per-step validation architecture manageable and well-tested. React and other component-based frameworks make the state sharing pattern clean with context or a global store.

5. Add runtime validation. Use Zod or Valibot to validate that the API actually sends what your types describe. The converter gives you structure; a schema library gives you runtime guarantees.

When writing runtime validation in TypeScript, Zod is usually the first choice.

Do you validate the data your backend API returns? Most people just use as to cast the type and hope the data matches expectations. Zod lets you define a schema once and get both runtime validation and TypeScript types — no more trust-based programming.

Zod is a TypeScript-first schema declaration and validation library. It is widely used in Next.js projects to bridge the gap between runtime data validation and compile-time type safety.

Every serious TypeScript project uses Zod for runtime validation. The team loves it — one schema, end-to-end type inference, parse-don't-validate all the way down. Done, sorted, move on.