picomcp is a TypeScript framework for building Model Context Protocol servers. An MCP server gives an AI application (Claude, Cursor, VS Code, your own agent) tools it can call, resources it can read and prompts it can offer to the user.
import { pico } from "picomcp";
import { z } from "zod";
pico({ name: "weather" })
.tool("forecast", {
description: "Get the forecast for a city.",
input: z.object({ city: z.string() }),
run: ({ city }) => getForecast(city),
})
.serve();That is a complete server. Run it with node server.ts and connect it to any MCP client.
Why picomcp#
- Small. About 4 kB gzipped, zero dependencies. The whole library fits in your head and in a code review.
- Typed from your schema. Pass a Zod, Valibot or ArkType schema. Your handler gets typed arguments, the client gets a JSON Schema, invalid calls get a readable error.
- Runs anywhere. Node 22+, Bun, Deno, Cloudflare Workers and every runtime with
fetch. No Node built-ins in the core. - Two transports, one server. stdio for desktop clients, Streamable HTTP for remote servers. Switch with one option.
- Testable without a client.
server.call("forecast", { city: "Lyon" })runs a tool in-process, fully typed. - Verified against the official SDK. The test suite drives picomcp with
@modelcontextprotocol/sdkover both transports.
What picomcp is not#
picomcp implements the server side of MCP and nothing else. It does not include a client, an agent loop or an LLM SDK. It favours the stateless request/response half of the protocol, which is what most servers need. Server-initiated features such as sampling and elicitation are not supported yet: see Protocol coverage for the exact list.
Next steps#
- Quickstart: build and connect a server in five minutes.
- Tools: everything a tool can do.
- For AI agents: give your coding agent the docs in one URL.