mcp

An integration that serves json-render interfaces as MCP Apps. MCP is a protocol that lets AI clients such as Claude, ChatGPT, Cursor, and VS Code connect to external tools and interactive views.

In plain words
What is it for?
Use it to build MCP servers with interactive json-render views, connect a React client inside the app frame, or expose generated interfaces to supported AI tools.
Why use it?
It provides a way for an MCP server to show an interactive interface inside an MCP-capable client rather than returning only text.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/vercel-labs/json-render/mcp
Any agent
npx skills add vercel-labs/json-render --skill mcp
Clone the repo
git clone --depth 1 https://github.com/vercel-labs/json-render

Made for: Claude Code, Codex.

Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 894 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00044 $0.00894
Opus 5 $0.00022 $0.00447
Sonnet 5 $0.00009 $0.00179
Haiku 4.5 $0.00004 $0.00089

Measured 2d ago against content hash 706ccd32b510, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

mcp scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/mcp/SKILL.md · 129 lines

How it starts

The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.

@json-render/mcp

MCP Apps integration that serves json-render UIs as interactive MCP Apps inside Claude, ChatGPT, Cursor, VS Code, and other MCP-capable clients.

Quick Start

Server (Node.js)

import { createMcpApp } from "@json-render/mcp";
import { defineCatalog } from "@json-render/core";
import { schema } from "@json-render/react/schema";
import { shadcnComponentDefinitions } from "@json-render/shadcn/catalog";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import fs from "node:fs";

const catalog = defineCatalog(schema, {
  components: { ...shadcnComponentDefinitions },
  actions: {},
});

const server = createMcpApp({
  name: "My App",
  version: "1.0.0",
  catalog,
  html: fs.readFileSync("dist/index.html", "utf-8"),
});

await server.connect(new StdioServerTransport());

Client (React, inside iframe)

import { useJsonRenderApp } from "@json-render/mcp/app";
import { JSONUIProvider, Renderer } from "@json-render/react";

function McpAppView({ registry }) {
  const { spec, loading, error } = useJsonRenderApp();
  if (error) return <div>Error: {error.message}</div>;
  if (!spec) return <div>Waiting...</div>;
  return (
    <JSONUIProvider registry={registry} initialState={spec.state ?? {}}>
      <Renderer spec={spec} registry={registry} loading={loading} />
    </JSONUIProvider>
  );
}

Architecture

  1. createMcpApp() creates an McpServer that registers a render-ui tool and a ui:// HTML resource
  2. The tool description includes the catalog prompt so the LLM knows how to generate valid specs
  3. The HTML resource is a Vite-bundled single-file React app with json-render renderers
  4. Inside the iframe, useJsonRenderApp() connects to the host via postMessage and renders specs

Server API

  • createMcpApp(options) - main entry, creates a full MCP server
  • registerJsonRenderTool(server, options) - register a json-render tool on an existing server
  • registerJsonRenderResource(server, options) - register the UI resource

Read the full file on GitHub · 129 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 129 lines · 44 tokens per session scan A 706ccd32b510

Subscribe to this mod's changes

mcp is a skill published in the GitHub repository vercel-labs/json-render (16,056 stars, last pushed 4d ago), licensed Apache-2.0. It adds 44 tokens to every session and 894 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens