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.
npx agentmods add skills/vercel-labs/json-render/mcpnpx skills add vercel-labs/json-render --skill mcpgit clone --depth 1 https://github.com/vercel-labs/json-renderWhat 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.
| Model | Per session | Once 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 |
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.
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
createMcpApp()creates anMcpServerthat registers arender-uitool and aui://HTML resource- The tool description includes the catalog prompt so the LLM knows how to generate valid specs
- The HTML resource is a Vite-bundled single-file React app with json-render renderers
- Inside the iframe,
useJsonRenderApp()connects to the host viapostMessageand renders specs
Server API
createMcpApp(options)- main entry, creates a full MCP serverregisterJsonRenderTool(server, options)- register a json-render tool on an existing serverregisterJsonRenderResource(server, options)- register the UI resource
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.
- 2d ago First seen · 129 lines · 44 tokens per session scan A 706ccd32b510
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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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.
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.
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.
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.
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…