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Official repo for spec & SDK of MCP Apps protocol - standard for UIs embedded AI chatbots, served by MCP servers
This repository page is useful for visitors, but Git-Stars keeps it out of search indexing until more original evidence and comparison context are available.
85
review score
Problem solved
Traditional MCP tools are limited to text-based responses, forcing users to interpret raw data or switch to external apps. MCP Apps solves this by providing a standard way to embed interactive UIs within chat clients, eliminating the need for custom integrations per client and enabling a consistent, rich user experience across multiple platforms.
Deployment reality
The available setup signal starts with: npm install -S @modelcontextprotocol/ext-apps. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is NOASSERTION. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build interactive data visualizations (e.g., live charts, dashboards), form-based workflows, and complex tool UIs that render inline in chat. The SDK includes agent skills to scaffold new apps, migrate existing OpenAI Apps, add UIs to existing MCP servers, and convert web apps to hybrid web+MCP apps. The ceiling is high: any UI that can be built for the web can be adapted to run inside a compliant chat client, enabling sophisticated data exploration, configuration, and control directly in the conversation.
Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.
modelcontextprotocol/ext-apps is tracked as a TypeScript project in the AI App, MCP Server, Developer Tool, UI Framework, Infrastructure area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 2.7k total stars, with +5 today, +0 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Moderate; the latest push was 31 days ago, and the open issue queue is 188, about 7.09% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 357 forks and 2.7k watchers suggest how often the project is reused or followed. License signal: NOASSERTION. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need AI prototypes, LLM workflows, and agent-style applications. Be cautious when you have low tolerance for large unresolved issue queues.
Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 5. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
LimitedLicense is unknown and should be checked before commercial use.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
2.7k Stars
Reuse
357 Forks
Attention
2.7k Watchers
Maintenance
moderate
License
NOASSERTION
Open issues
188
MCP Apps is a framework for building interactive UIs for MCP tools, such as charts, forms, and dashboards, that render inline in chat clients like Claude and ChatGPT. It extends the Model Context Protocol to allow tools to declare UI resources, which hosts render in sandboxed iframes with bidirectional communication.
Key Features
- Interactive UIs (charts, forms, dashboards) rendered inline in chat clients - Extends MCP with UI resources and bidirectional communication between host and UI - Includes Agent Skills for scaffolding, migrating, and converting apps
AI App
End-user AI applications and AI-native product examples
MCP Server
Model Context Protocol servers, clients, and integrations
Developer Tool
Tools that improve coding, testing, build, and local workflow
UI Framework
Frontend frameworks, design systems, and interface libraries
Infrastructure
Cloud, deployment, networking, containers, and platform tooling
npm install -S @modelcontextprotocol/ext-apps63
Health Score
Moderate
Commit Activity
Nov 21, 2025
Created
Jul 23, 2026
Last push
+0
Today's growth
+0
7-day growth
+0
30-day growth
Forks
Open
Watchers
modelcontextprotocol
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openclaw/openclaw
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
✓
License
✓
Forked
~ Moderate
Maintained
Problem Solved
Traditional MCP tools are limited to text-based responses, forcing users to interpret raw data or switch to external apps. MCP Apps solves this by providing a standard way to embed interactive UIs within chat clients, eliminating the need for custom integrations per client and enabling a consistent, rich user experience across multiple platforms.
Capabilities
Developers can build interactive data visualizations (e.g., live charts, dashboards), form-based workflows, and complex tool UIs that render inline in chat. The SDK includes agent skills to scaffold new apps, migrate existing OpenAI Apps, add UIs to existing MCP servers, and convert web apps to hybrid web+MCP apps. The ceiling is high: any UI that can be built for the web can be adapted to run inside a compliant chat client, enabling sophisticated data exploration, configuration, and control directly in the conversation.
Bottom Line
MCP Apps is a forward-looking standard that bridges the gap between AI chat and rich UI, ideal for developers building interactive tools for AI assistants and organizations wanting to deploy data-driven apps across multiple chat platforms. It should be avoided by those needing minimal dependencies or who target clients that don't yet support the spec. The key trade-off is added complexity for enhanced user experience and cross-client portability.