Install
$ agentstack add mcp-mcp-widgets-examples ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ● Environment & secrets Used
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
AI-Powered Chat Application with MCP Widgets
Introducing Model Context Protocol (MCP) Widgets, server side rendered UI snippets that are delivered as part of the MCP response to the client application.
This project demonstrates a modern AI chat application that uses the MCP to enable rich, interactive responses beyond just text. The application integrates specialized MCP widgets to display structured data like weather forecasts and product listings.
Request for comments
Check out the RFC repository.
Example video
Use the two example prompts to trigger the ecommerce or weather MCP and receive UI widgets. https://github.com/user-attachments/assets/9f958dfb-600c-4f48-a44b-ca80cd2d4c02
System Architecture
The application consists of three main components:
- AI Chatbot - The frontend chat interface where users interact with the AI
- Weather MCP - A specialized service providing weather forecasts with visual displays
- E-commerce MCP - A specialized service for product discovery and browsing
Features
Weather MCP
- Fetch weather forecasts for any location using coordinates
- Display beautiful visual weather widgets
- Show temperature, forecast, wind conditions, and more
- Responsive design that works on mobile and desktop
E-commerce MCP
- Product search and filtering capabilities
- Category browsing
- Product recommendations
- Interactive product cards with images, price, ratings, and availability
- Responsive grid layout for multiple products
How It Works
This application uses the Model Context Protocol (MCP) to bridge AI models with specialized UI components:
- User queries are sent to the AI model
- When weather or shopping-related queries are detected, the AI invokes the appropriate MCP
- The MCP processes the request and returns HTML/CSS rendering along with minimal text data
- The chat interface displays the rich visual content to the user
Configuration
Changing AI Models
You can easily switch AI models by updating the providers.ts file:
export const myProvider = isTestEnvironment
? customProvider({
languageModels: {
'chat-model': chatModel,
'chat-model-reasoning': reasoningModel,
'title-model': titleModel,
'artifact-model': artifactModel,
},
})
: customProvider({
languageModels: {
'chat-model': openai('gpt-4o'), // Change this to use a different model
'chat-model-reasoning': wrapLanguageModel({
model: openai('gpt-4o'), // Change this to use a different model
middleware: extractReasoningMiddleware({ tagName: 'think' }),
}),
'title-model': openai('gpt-4o'), // Change this to use a different model
'artifact-model': openai('gpt-4o'), // Change this to use a different model
},
});
API Keys
To use OpenAI models, you'll need to set up your API key:
- Create a
.env.localfile in the root directory - Add your OpenAI API key:
`` OPENAI_API_KEY=your_api_key_here ``
Development
Starting the Application
# Install dependencies
npm install
# Start the development server
npm run dev
Building MCP Widgets
Each MCP must be built separately:
# Build the Weather MCP
cd apps/weather-mcp
npm run build
# Build the E-commerce MCP
cd apps/ecommerce-mcp
npm run build
Extending the System
You can add more MCPs by:
- Creating a new MCP service in the
apps/directory - Implementing the Model Context Protocol
- Adding visualization components
- Registering it with the MCP clients in `apps/ai-chatbo/lib/tools
This architecture can be extended to support many types of interactive widgets beyond weather and e-commerce, such as calendars, maps, charts, and more.
Acknowledgements
This project is heavily relying on the work the anthopic team did with the Model Context Protocol and the Vercel's team of making AI tools accessible via their AI SDK and their example project Vercel AI Chatbot.
Source & license
This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: mcp-widgets
- Source: mcp-widgets/examples
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.