mcp-visual-output

Interactive MCP visual output via @json-render/mcp: upgrade plain JSON tool responses to dashboards rendered in sandboxed iframes inside MCP clients like Claude, Cursor, and ChatGPT. Use when a tool result would read better as a stat grid, data table, or status badge than as text. For the server itself (transport, auth, tool handlers, security) reach for ork:mcp-patterns.

Ir a la instalación

Repositorio: yonatangross/orchestkit
Última actividad en el origen: 30 de julio de 2026 a las 07:35
Idioma detectado de SKILL.md: inglés
Estrellas: 219
Forks: 20

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Revisa los archivos de origen

Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

Volver a la búsqueda Volver a la instalación

name	mcp-visual-output
compatibility	Claude Code 2.1.220+
description	Interactive MCP visual output via @json-render/mcp: upgrade plain JSON tool responses to dashboards rendered in sandboxed iframes inside MCP clients like Claude, Cursor, and ChatGPT. Use when a tool result would read better as a stat grid, data table, or status badge than as text. For the server itself (transport, auth, tool handlers, security) reach for ork:mcp-patterns.
tags	["mcp","json-render","visual-output","dashboard","iframe","sandbox","interactive-ui","genui"]
version	1.1.0
author	OrchestKit
user-invocable	false
disable-model-invocation	false
complexity	medium
context	inherit
persuasion-type	guidance
metadata	{"category":"mcp","upstream-package":"@json-render/mcp","upstream-version-tested":"0.19.0"}

MCP Visual Output

Upgrade plain MCP tool responses to interactive dashboards rendered inside AI conversations. Built on @json-render/mcp, which bridges the json-render spec system with MCP's tool/resource model -- the AI generates a typed JSON spec, and a sandboxed iframe renders it as an interactive UI.

Building an MCP server from scratch? Use ork:mcp-patterns for server setup, transport, and security. This skill focuses on the visual output layer after your server is running.

Need the full component catalog? See ork:json-render-catalog for all available components, props, and composition patterns.

Decision Tree -- Which File to Read

What are you doing?
|
+-- Setting up visual output for the first time
|   +-- New MCP server -----------> rules/mcp-app-setup.md
|   +-- Existing MCP server ------> rules/mcp-app-setup.md (registerJsonRenderTool section)
|
+-- Configuring security / sandbox
|   +-- CSP declarations ----------> rules/sandbox-csp.md
|   +-- Iframe permissions --------> rules/sandbox-csp.md
|
+-- Rendering strategy
|   +-- Progressive streaming -----> rules/streaming-output.md
|   +-- Dashboard layouts ----------> rules/dashboard-patterns.md
|
+-- API reference
|   +-- Server-side API -----------> references/mcp-integration.md
|   +-- Component recipes ----------> references/component-recipes.md

Quick Reference

Category	Rule	Impact	Key Pattern
Setup	`mcp-app-setup.md`	HIGH	createMcpApp() and registerJsonRenderTool()
Security	`sandbox-csp.md`	HIGH	CSP declarations, iframe sandboxing
Rendering	`streaming-output.md`	MEDIUM	Progressive rendering via JSON Patch
Patterns	`dashboard-patterns.md`	MEDIUM	Stat grids, status badges, data tables

Total: 4 rules across 3 categories

How It Works

Define a catalog -- typed component schemas using defineCatalog() + Zod
Register with MCP -- for new servers or for existing ones

createMcpApp()

registerJsonRenderTool()

import { createMcpApp } from '@json-render/mcp'
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'
import { buildAppHtml } from '@json-render/mcp/app'
import { catalog } from './catalog'

// Generate the iframe HTML from the bundled JS/CSS (docs-prescribed generator).
const bundledHtml = buildAppHtml({ entry: './app.tsx' })

// 1. Create the MCP app (async; returns an McpServer, no .start()/.close()).
//    name + version are required; tool config nests under `tool`
//    (default tool name is 'render-ui'). There is no top-level `csp`.
const server = await createMcpApp({
  name: 'my-app',
  version: '1.0.0',
  catalog,           // component schemas the AI can use
  html: bundledHtml, // pre-built iframe app (single HTML file)
  tool: {
    name: 'render-dashboard',
    description: 'Render an interactive dashboard from a json-render spec',
  },
})

// 2. Connect a transport -- stdio, Streamable HTTP, or any MCP transport
await server.connect(new StdioServerTransport())

import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js'
import { registerJsonRenderTool, registerJsonRenderResource } from '@json-render/mcp'
import { buildAppHtml } from '@json-render/mcp/app'
import { catalog } from './catalog'

const server = new McpServer({ name: 'my-server', version: '1.0.0' })

// Generate the iframe HTML from the bundled JS/CSS (docs-prescribed generator).
const bundledHtml = buildAppHtml({ entry: './app.tsx' })

const resourceUri = 'ui://my-server/dashboard'

// Register the render tool (lets the model return specs).
// name, title, description, and resourceUri are all required.
registerJsonRenderTool(server, {
  catalog,
  name: 'render-dashboard',
  title: 'Render Dashboard',
  description: 'Render an interactive dashboard from a json-render spec',
  resourceUri,
})

// Serve the bundled HTML iframe app as a resource (new in 0.15).
// resourceUri must match the tool's resourceUri.
registerJsonRenderResource(server, { resourceUri, html: bundledHtml })

import { useJsonRenderApp } from '@json-render/mcp/app'
import { Renderer } from '@json-render/react'
import { registry } from './registry'

function App() {
  const { spec, loading } = useJsonRenderApp()
  if (loading) return <Skeleton />
  return <Renderer spec={spec} registry={registry} />
}

import { defineCatalog } from '@json-render/core'
import { schema } from '@json-render/react/schema'
import { z } from 'zod'

export const dashboardCatalog = defineCatalog(schema, {
  components: {
    StatGrid: {
      props: z.object({
        items: z.array(z.object({
          label: z.string(),
          value: z.string(),
          trend: z.enum(['up', 'down', 'flat']).optional(),
          color: z.enum(['green', 'red', 'yellow', 'blue']).optional(),
        })),
      }),
      children: false,
    },
    StatusBadge: {
      props: z.object({
        label: z.string(),
        status: z.enum(['success', 'warning', 'error', 'info', 'pending']),
      }),
      children: false,
    },
    DataTable: {
      props: z.object({
        columns: z.array(z.object({ key: z.string(), label: z.string() })),
        rows: z.array(z.record(z.string())),
      }),
      children: false,
    },
  },
})

{
  "root": "dashboard",
  "elements": {
    "dashboard": {
      "type": "Card",
      "props": { "title": "Eval Results -- v7.21.1" },
      "children": ["stats", "table"]
    },
    "stats": {
      "type": "StatGrid",
      "props": {
        "items": [
          { "label": "Skills Evaluated", "value": "94", "trend": "flat" },
          { "label": "Pass Rate", "value": "97.8%", "trend": "up", "color": "green" },
          { "label": "Avg Score", "value": "8.2/10", "trend": "up" }
        ]
      }
    },
    "table": {
      "type": "DataTable",
      "props": {
        "columns": [
          { "key": "skill", "label": "Skill" },
          { "key": "score", "label": "Score" },
          { "key": "status", "label": "Status" }
        ],
        "rows": [
          { "skill": "implement", "score": "9.1", "status": "pass" },
          { "skill": "verify", "score": "8.7", "status": "pass" }
        ]
      }
    }
  }
}

Decision	Recommendation
New vs existing server	`createMcpApp()` for new; `registerJsonRenderTool()` to add to existing
CSP policy	Minimal -- only declare domains you actually need
Streaming	Always enable progressive rendering; never wait for full spec
Dashboard depth	Keep element trees flat (2-3 levels max) for streamability
Component count	3-5 component types per catalog covers most dashboards
Visual vs text	Use visual output for multi-metric views; plain text for single values

Scenario	Use Visual Output	Use Plain Text
Multiple metrics at a glance	Yes -- StatGrid	No
Tabular data (5+ rows)	Yes -- DataTable	No
Status of multiple systems	Yes -- StatusBadge grid	No
Single value answer	No	Yes
Error message	No	Yes
File content / code	No	Yes