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Introducing MCP UI, the SDK that wires UI components directly into the MCP protocol🤯 Watch the video to see the new phase in AI interaction! Why browse when you can have universal apps that deliver the right UI to consume any data and perform any action? Replace cumbersome walls... show more

Ido Salomon

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79,457 görüntüleme • 1 yıl önce •via X (Twitter)

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Finally, MCP servers can now deliver UI-rich experiences! MCP servers in Claude/Cursor don't offer UI any experience yet, like charts. It's just text/JSON. mcp-ui lets you add interactive web components to its output that can be rendered by the MCP client. 100% open-source!

Finally, MCP servers can now deliver UI-rich experiences! MCP servers in Claude/Cursor don't offer UI any experience yet, like charts. It's just text/JSON. mcp-ui lets you add interactive web components to its output that can be rendered by the MCP client. 100% open-source!

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Create dynamic and interactive Agent UI experiences in the Gemini Enterprise app. With support for the Agent-to-UI protocol (A2UI), custom agents can dynamically generate rich, native UI components directly within the Gemini Enterprise app.

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10,616 görüntüleme • 1 yıl önce

Comment “MCP” and I’ll DM you the servers I use every day Browser MCP tests apps for you, Magic MCP builds UI in seconds, and GitMCP links your AI to any repo. These are only 3 of the seven MCPs I use. Once you try them, you’ll never want to build with AI without them.

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Prajwal Tomar

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New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

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Rabi Shanker Guha

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Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,332 görüntüleme • 9 ay önce

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In case you missed it, earlier this week we launched "MCP: Build Rich-Context AI Apps with Anthropic." Enroll now and: ✅ Explore how MCP standardizes access to tools and data for AI applications, its underlying architecture, and how it simplifies the integration of new tools and connections to external systems (e.g., GitHub repos, Google Docs, local files). ✅ Build and deploy an MCP server that provides tools, resources, and prompts, and add it to the configuration of AI applications, such as Claude Desktop, to extend them. ✅ Build an MCP-compatible application that hosts multiple MCP clients, each maintaining 1-to-1 connection to an MCP server. Join in for free:

DeepLearning.AI

23,829 görüntüleme • 1 yıl önce

We'll be open sourcing an LLM → UI streaming mechanism powered by the Vercel AI SDK and RSC. It will help you build rich AI apps that go beyond text and markdown, with optimal performance and robustness to UI state changes.

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Guillermo Rauch

207,316 görüntüleme • 2 yıl önce

Finally, we have published Horizon OS UI Set for ISDK-Unity and Figma with the Interaction SDK v69 release on 10/9! This is our first public release of official UI components and design resources based on the Quest Design System. UI Set for Meta XR Interaction SDK: Figma Design file: By using UI Set, you can achieve consistent input modality support, visual feedback, and interaction behaviors. In addition to UI components and common UI pattern examples, theming capability can help you bring your own brand identity into your UI. We hope these UI Set Unity components + Figma design assets + Guideline docs - can help you designers, developers, and creators design, experiment, and build great spatial app experiences for Horizon OS platform. Including a personal blog post - How to build a simple spatial panel app with Meta Horizon OS UI Set, an image viewer with NASA Mars Rover API: #MetaQuest #MixedReality #MetaHorizonOS #InteractionSDK #MetaXRSDK #SpatialComputing

Yoon Park (박동윤)

59,115 görüntüleme • 1 yıl önce

We are witnessing a fundamental shift in how software is built. Over the decades, UI was static and predictable. But AI is changing the fundamental nature of the interface. We are moving away from fixed UI and toward a new standard: Generative UI. Generative UI lets agents participate in the UI, not just the conversation. It’s a dynamic participant in the user experience. Depending on the complexity of your app, Generative UI generally falls into three categories: 1. Static (AG-UI): UI is chosen from a fixed set of hand-built components 2. Open-Ended (MCP Apps): agent sends arbitrary content like raw HTML or iframes for total flexibility. 3. Declarative (A2UI): agent and frontend communicate via a structured UI spec for dynamic widgets and forms. The AG-UI Protocol by CopilotKit is the real breakthrough. AG-UI acts as the universal transport layer. It provides the integrations and middleware for every Gen-UI spec, making it trivial to bring dynamic components into your existing app. CopilotKit provides the go-to ecosystem for building agentic applications and is the React client of AG-UI. It is designed to be deeply integrated, allowing developers to bring their own frameworks or protocols to the table. Play around with different types of Generative UI here → Check out the video attached to see Generative UI in action. Appreciate the CopilotKit team for collaborating with me on this deep dive.

We are witnessing a fundamental shift in how software is built. Over the decades, UI was static and predictable. But AI is changing the fundamental nature of the interface. We are moving away from fixed UI and toward a new standard: Generative UI. Generative UI lets agents participate in the UI, not just the conversation. It’s a dynamic participant in the user experience. Depending on the complexity of your app, Generative UI generally falls into three categories: 1. Static (AG-UI): UI is chosen from a fixed set of hand-built components 2. Open-Ended (MCP Apps): agent sends arbitrary content like raw HTML or iframes for total flexibility. 3. Declarative (A2UI): agent and frontend communicate via a structured UI spec for dynamic widgets and forms. The AG-UI Protocol by CopilotKit is the real breakthrough. AG-UI acts as the universal transport layer. It provides the integrations and middleware for every Gen-UI spec, making it trivial to bring dynamic components into your existing app. CopilotKit provides the go-to ecosystem for building agentic applications and is the React client of AG-UI. It is designed to be deeply integrated, allowing developers to bring their own frameworks or protocols to the table. Play around with different types of Generative UI here → Check out the video attached to see Generative UI in action. Appreciate the CopilotKit team for collaborating with me on this deep dive.

Pratham

33,920 görüntüleme • 6 ay önce

🤖 Introducing our Model Context Protocol (MCP) Server The MCP protocol standardizes how apps provide context to LLMs. No more training LLMs — just connect them directly to our blockchain APIs for live token prices, transfers, NFT data, and more 💪 Build smarter AI agents today! Let’s gooooooo 🔗 -------------- TIMESTAMPS 0:00 Setting up the configuration file 0:30 Add configuration to Cursor 1:06 Executing prompts for the price of ETH 2:25 Getting data on Vitalik’s transfers 3:45 Contributing or accessing our MCP server

🤖 Introducing our Model Context Protocol (MCP) Server The MCP protocol standardizes how apps provide context to LLMs. No more training LLMs — just connect them directly to our blockchain APIs for live token prices, transfers, NFT data, and more 💪 Build smarter AI agents today! Let’s gooooooo 🔗 -------------- TIMESTAMPS 0:00 Setting up the configuration file 0:30 Add configuration to Cursor 1:06 Executing prompts for the price of ETH 2:25 Getting data on Vitalik’s transfers 3:45 Contributing or accessing our MCP server

Alchemy

11,523 görüntüleme • 1 yıl önce

⚡day 5 of launch week: from (zero) MCP server to ChatGPT app this is peak developer experience 🤌🏼 1. define your UI in a single react file. 2. test it instantly with hot reload in the built-in Inspector 3. deploy it in one click on mcp-use Cloud 4. ship it directly to ChatGPT with: npx create-mcp-use-app@latest my-server --template apps-sdk you can go from zero to a deployed mcp server with apps sdk widgets in just 5 minutes here's how 🧵

⚡day 5 of launch week: from (zero) MCP server to ChatGPT app this is peak developer experience 🤌🏼 1. define your UI in a single react file. 2. test it instantly with hot reload in the built-in Inspector 3. deploy it in one click on mcp-use Cloud 4. ship it directly to ChatGPT with: npx create-mcp-use-app@latest my-server --template apps-sdk you can go from zero to a deployed mcp server with apps sdk widgets in just 5 minutes here's how 🧵

pederzh

22,647 görüntüleme • 9 ay önce

introducing executor artifacts! - open source claude artifacts - bring your artifacts to any harness - embeds directly in claude code and codex - can call any API / MCP / integration you have added live now in the latest executor update

introducing executor artifacts! - open source claude artifacts - bring your artifacts to any harness - embeds directly in claude code and codex - can call any API / MCP / integration you have added live now in the latest executor update

Rhys

42,214 görüntüleme • 10 gün önce

this has been the most requested feature for years now. FlutterFlow MCP is here. you can now bring your agents directly into FlutterFlow and switch between them: Claude Code, Gemini CLI, Codex, or any MCP-compatible client. can’t wait to see what y’all build.

this has been the most requested feature for years now. FlutterFlow MCP is here. you can now bring your agents directly into FlutterFlow and switch between them: Claude Code, Gemini CLI, Codex, or any MCP-compatible client. can’t wait to see what y’all build.

Abel Mengistu

686,500 görüntüleme • 3 ay önce

Sharing the 4 Claude MCP servers I use the most lately. 1. TinyFish MCP lets Claude browse the live web, scrape, research, and return structured data instead of static answers. I’ve been using it to gather AI news for my newsletter, like pulling top Hacker News posts from the last few hours and summarizing them into a clean list. 2. GitHub MCP helps me review commits, trace changes, and understand repos without jumping between tools. 3. Figma MCP lets me inspect designs, check layout and spacing, and pull UI details directly into the conversation. 4. Excalidraw MCP is great for thinking visually. I use it to sketch flows and system ideas when text isn’t enough. The last few are probably familiar, but the first one is worth a look. I recorded a short video showing how it works in practice.

Sharing the 4 Claude MCP servers I use the most lately. 1. TinyFish MCP lets Claude browse the live web, scrape, research, and return structured data instead of static answers. I’ve been using it to gather AI news for my newsletter, like pulling top Hacker News posts from the last few hours and summarizing them into a clean list. 2. GitHub MCP helps me review commits, trace changes, and understand repos without jumping between tools. 3. Figma MCP lets me inspect designs, check layout and spacing, and pull UI details directly into the conversation. 4. Excalidraw MCP is great for thinking visually. I use it to sketch flows and system ideas when text isn’t enough. The last few are probably familiar, but the first one is worth a look. I recorded a short video showing how it works in practice.

Tw93

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