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

1. Define a catalog -- typed component schemas using defineCatalog() + Zod
2. Register with MCP -- createMcpApp() for new servers or registerJsonRenderTool() for existing ones
3. AI generates specs -- the model produces a JSON spec conforming to the catalog
4. Iframe renders it -- a bundled React app inside a sandboxed iframe renders the spec with useJsonRenderApp() + <Renderer />

The AI never writes HTML or CSS. It produces a structured JSON spec that references catalog components by type. The iframe app renders those components using a pre-built registry.

Quick Start -- New MCP Server

import { createMcpApp } from '@json-render/mcp'
import { catalog } from './catalog'
import bundledHtml from './app.html'

// 1. Create the MCP app (wraps McpServer + registers the render tool)
const app = createMcpApp({
  catalog,           // component schemas the AI can use
  html: bundledHtml, // pre-built iframe app (single HTML file)
})

// 2. Start -- works with stdio, Streamable HTTP, or any MCP transport
app.start()

Quick Start -- Enhance Existing Server with Visual Output

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

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

// Register the render tool (lets the model return specs)
registerJsonRenderTool(server, { catalog })

// Serve the bundled HTML iframe app as a resource (new in 0.15)
registerJsonRenderResource(server, { html: bundledHtml })

registerJsonRenderResource() was added in 0.15 to separate tool registration from UI resource serving — useful when the host caches the bundled HTML (clients: Claude, ChatGPT, Cursor, VS Code Copilot, Goose, Postman). Transports: stdio and Streamable HTTP (Express) both supported.

Client-Side Iframe App

The iframe app receives specs from the MCP host and renders them:

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} />
}

Catalog Definition

Catalogs define what components the AI can use. Each component has typed props via Zod:

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

export const dashboardCatalog = defineCatalog({
  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,
  },
})

Example: Eval Results Dashboard

The AI generates a spec like this -- flat element map, no nesting beyond 2 levels:

{
  "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" }
        ]
      }
    }
  }
}

Key Decisions

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

CC 2.1.113 fixed MCP concurrent-call timeout handling — hanging tool calls now error cleanly instead of blocking the queue. Parallel tool invocation from dashboards is safer; no workarounds needed.

When to Use Visual Output vs Plain Text

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

Common Mistakes

1. Returning raw HTML strings from MCP tools instead of json-render specs (breaks type safety, no streaming)
2. Deeply nested component trees that cannot stream progressively (keep flat)
3. Using script-src 'unsafe-inline' in CSP declarations (security risk, unnecessary)
4. Waiting for the full spec before rendering (defeats progressive rendering)
5. Defining 20+ component types in a single catalog (increases prompt token cost)
6. Missing html bundle in createMcpApp() config (iframe has nothing to render)

Related Skills

• ork:mcp-patterns -- MCP server building, transport, security
• ork:json-render-catalog -- Full component catalog and composition patterns
• ork:multi-surface-render -- Rendering across Claude, Cursor, ChatGPT, web
• ork:ai-ui-generation -- GenUI patterns for AI-generated interfaces