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Android is shifting to an intelligence system. 🧠 This evolution gives developers a spectrum of ways to engage users. Out of the box, Gemini can automate tasks across your app by analyzing the screen with zero code required. For deeper integration, the AppFunctions Jetpack library provides an MCP-like solution.... show more

Android Developers

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28,944 views • 1 month ago •via X (Twitter)

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Android AppFunctions 🤝 on-device MCP You can create self-describing functions using the AppFunctions Jetpack library to let assistants like Gemini execute tasks via natural language. Connect your app to AI agents with Android AppFunctions →

Android AppFunctions 🤝 on-device MCP You can create self-describing functions using the AppFunctions Jetpack library to let assistants like Gemini execute tasks via natural language. Connect your app to AI agents with Android AppFunctions →

Android Developers

27,211 views • 4 months ago

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

Andrew Ng

81,696 views • 10 months ago

The new Codex is another jump in what agents will look like for knowledge workers. Agents that can code, work with tools, and use computers, can begin to execute long running tasks in the background for all areas of work. This can mean drafting reports, setting up data rooms for a merger, reviewing contracts, helping onboard clients, generating marketing assets, processing invoices, and more. With the Box plugin inside of the new Codex, you can begin to automate almost any kind of work with enterprise content. And importantly, being able to work across multiple apps is a huge point of leverage because we can now far more easily connect our tools together.

The new Codex is another jump in what agents will look like for knowledge workers. Agents that can code, work with tools, and use computers, can begin to execute long running tasks in the background for all areas of work. This can mean drafting reports, setting up data rooms for a merger, reviewing contracts, helping onboard clients, generating marketing assets, processing invoices, and more. With the Box plugin inside of the new Codex, you can begin to automate almost any kind of work with enterprise content. And importantly, being able to work across multiple apps is a huge point of leverage because we can now far more easily connect our tools together.

Aaron Levie

71,589 views • 3 months ago

The Box MCP server is live in the Box app in Matt Slack's Slackbot and is coming soon to Salesforce Agentforce. This brings the full power of our MCP tools to the forefront of the Salesforce AI ecosystem. Enterprise teams can now seamlessly orchestrate workflows across Box, Salesforce, and Slack, while preparing to build custom agents grounded in Box content via Agentforce. Your content. Your permissions. Any AI agent. Right where your team already works.

The Box MCP server is live in the Box app in Matt Slack's Slackbot and is coming soon to Salesforce Agentforce. This brings the full power of our MCP tools to the forefront of the Salesforce AI ecosystem. Enterprise teams can now seamlessly orchestrate workflows across Box, Salesforce, and Slack, while preparing to build custom agents grounded in Box content via Agentforce. Your content. Your permissions. Any AI agent. Right where your team already works.

Box

255,554 views • 14 days ago

Jupyter Agents - LLMs running data analysis directly in a notebook! The agent can load data, execute code, plot results and following your guidance and ideas! A very natural way to collaborate with an LLM over data and it's just scratching the surface of what's possible soon!

Jupyter Agents - LLMs running data analysis directly in a notebook! The agent can load data, execute code, plot results and following your guidance and ideas! A very natural way to collaborate with an LLM over data and it's just scratching the surface of what's possible soon!

Leandro von Werra

200,291 views • 1 year ago

wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.

wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.

Molly O’Shea

20,408 views • 1 year ago

Somebody hooked up Claude Code to pair program with o3 and Gemini 2.5 via MCP for an all-star coding lineup 🤯 - Claude controls the work - Can call out to Gemini and o3 for tasks and input - Works around MCP limit by using prompt files Check out Zen MCP (link below)

Somebody hooked up Claude Code to pair program with o3 and Gemini 2.5 via MCP for an all-star coding lineup 🤯 - Claude controls the work - Can call out to Gemini and o3 for tasks and input - Works around MCP limit by using prompt files Check out Zen MCP (link below)

Ian Nuttall

216,556 views • 1 year ago

We just open-sourced Simulang, a JavaScript library that gives you and your coding agents the ability to fully control computers. Simulang is our scripting framework for desktop automation. It allows you to write deterministic computer use code that can be replayed dozens if not hundreds of times. Much faster and cheaper, compared to LLM agents. It ships with a skill out of the box. That means you can turn natural language prompts into Simulang code directly, and your agent can now see the screen, click, type, and navigate. It gives your AI agents eyes and hands to control the computer like humans do. Try these things with Simulang: - upload your pile of invoices to the expense platform - find 50 people with specific criteria on LinkedIn - open your calculator app and do some math - find all buttons on your favorite app using accessibility tree - or draw a bunny with MS paint 🐰 In this demo, we show how Simulang took over when the coding agent couldn’t open and click on the target LinkedIn profile page. Give it a try:

Simular

14,362 views • 2 months ago

New course: Gemini CLI: Code & Create with an Open-Source Agent, built with Google Cloud Tech/Gemini CLI and taught by Jack Wotherspoon. Agentic coding assistants like Gemini CLI are transforming how developers work. This short course teaches you to use Google's open-source agent to coordinate local tools and cloud services for coding and non-coding workflows. Gemini CLI works from your terminal, so it works with your local files and development tools. You can also connect it to services through MCP. Then provide high-level instructions, and it autonomously plans and executes complex workflows. Skills you'll gain: - Build website features and automate code reviews with GitHub ActionsCreate data dashboards that combine local files with cloud data sources - Use MCP servers and extensions to orchestrate workflows across GitHub, Canva, and Google Workspace - Generate social media content from multimedia files like conference recordings I particularly appreciate that Gemini CLI is open-source. You can see exactly how it works, read the prompts it uses, and understand its architecture. The community has contributed thousands of pull requests. Since Gemini 3’s release I've found Gemini CLI highly capable - this is a tool worth having in your toolbox! Whether you're prototyping applications, automating workflows, or working with multimedia content, join to learn to delegate complex tasks and build faster:

New course: Gemini CLI: Code & Create with an Open-Source Agent, built with Google Cloud Tech/Gemini CLI and taught by Jack Wotherspoon. Agentic coding assistants like Gemini CLI are transforming how developers work. This short course teaches you to use Google's open-source agent to coordinate local tools and cloud services for coding and non-coding workflows. Gemini CLI works from your terminal, so it works with your local files and development tools. You can also connect it to services through MCP. Then provide high-level instructions, and it autonomously plans and executes complex workflows. Skills you'll gain: - Build website features and automate code reviews with GitHub ActionsCreate data dashboards that combine local files with cloud data sources - Use MCP servers and extensions to orchestrate workflows across GitHub, Canva, and Google Workspace - Generate social media content from multimedia files like conference recordings I particularly appreciate that Gemini CLI is open-source. You can see exactly how it works, read the prompts it uses, and understand its architecture. The community has contributed thousands of pull requests. Since Gemini 3’s release I've found Gemini CLI highly capable - this is a tool worth having in your toolbox! Whether you're prototyping applications, automating workflows, or working with multimedia content, join to learn to delegate complex tasks and build faster:

Andrew Ng

124,823 views • 6 months ago

With Google Flow Tools, you can now use natural language to create bespoke tools and workflows. In this video, you'll see how you can: ➡️ Explore and try the various Tools within Google Flow ➡️ Remix a tool to make it even more relevant for your unique workflow ➡️Create your own tool from scratch with a natural language prompt ➡️ Share your tools with a friend for them to try out Whether you’re looking for an image editor, video resizer or custom shaders, you can now create them in Google Flow, no coding experience required.

With Google Flow Tools, you can now use natural language to create bespoke tools and workflows. In this video, you'll see how you can: ➡️ Explore and try the various Tools within Google Flow ➡️ Remix a tool to make it even more relevant for your unique workflow ➡️Create your own tool from scratch with a natural language prompt ➡️ Share your tools with a friend for them to try out Whether you’re looking for an image editor, video resizer or custom shaders, you can now create them in Google Flow, no coding experience required.

Google Flow

19,473 views • 1 month ago

Gemini for Android is here! This new app makes it easier to access Google's chatbot, powered by the Gemini Pro LLM, right from your Android device. You can ask Gemini a question by tapping the launcher icon or long-pressing the power button. Once invoked, the Gemini overlay lets you enter a text or text+image prompt. You can tap the camera icon to snap a photo or press the "add this screen" button to take a screenshot of the current page to include in your prompt. The Gemini app for Android is available on Google Play with support for English, but support for Korean and Japanese will be coming next week. While there won't be an app for iOS, iPhone users can access Gemini by opening the Google App and tapping the "Gemini" button up top. How can you long-press the power button to invoke Gemini if that gesture is handled by Google Assistant? The answer is that the Gemini Android app can replace Google Assistant as your default assistant if you want, meaning all the ways you'd normally invoke Google Assistant on your phone can instead invoke Gemini. Unfortunately, Gemini currently doesn't offer ALL the same functionality as Google Assistant and requires an active data connection, but more features will be added over time. There's also now a Gemini Advanced tier, which offers access to Google's most powerful Ultra 1.0 LLM. Access to Gemini Advanced requires a subscription to the new $20/month "AI Premium" Google One plan, which offers the same benefits as the 2TB plan but adds access to Gemini Advanced and soon Gemini features in Gmail, Docs, & other Workspace apps (formerly under the Duet AI umbrella). Gemini Advanced is available in English on the web.

Gemini for Android is here! This new app makes it easier to access Google's chatbot, powered by the Gemini Pro LLM, right from your Android device. You can ask Gemini a question by tapping the launcher icon or long-pressing the power button. Once invoked, the Gemini overlay lets you enter a text or text+image prompt. You can tap the camera icon to snap a photo or press the "add this screen" button to take a screenshot of the current page to include in your prompt. The Gemini app for Android is available on Google Play with support for English, but support for Korean and Japanese will be coming next week. While there won't be an app for iOS, iPhone users can access Gemini by opening the Google App and tapping the "Gemini" button up top. How can you long-press the power button to invoke Gemini if that gesture is handled by Google Assistant? The answer is that the Gemini Android app can replace Google Assistant as your default assistant if you want, meaning all the ways you'd normally invoke Google Assistant on your phone can instead invoke Gemini. Unfortunately, Gemini currently doesn't offer ALL the same functionality as Google Assistant and requires an active data connection, but more features will be added over time. There's also now a Gemini Advanced tier, which offers access to Google's most powerful Ultra 1.0 LLM. Access to Gemini Advanced requires a subscription to the new $20/month "AI Premium" Google One plan, which offers the same benefits as the 2TB plan but adds access to Gemini Advanced and soon Gemini features in Gmail, Docs, & other Workspace apps (formerly under the Duet AI umbrella). Gemini Advanced is available in English on the web.

Mishaal Rahman

38,428 views • 2 years ago

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 views • 10 months ago

Agent-in-the-browser unlocks superpowers. The magic really shines when you bring your own model, system prompts, and MCP tools into the mix. Hence, AgentBoard 🪄. Your models, keys, and settings. Integration with remote MCP servers and — this is where it gets really fun — ability to register and allow the agent to call custom ~WebMCP tools that execute in context of the page. Chrome Web Store: Code:

Agent-in-the-browser unlocks superpowers. The magic really shines when you bring your own model, system prompts, and MCP tools into the mix. Hence, AgentBoard 🪄. Your models, keys, and settings. Integration with remote MCP servers and — this is where it gets really fun — ability to register and allow the agent to call custom ~WebMCP tools that execute in context of the page. Chrome Web Store: Code:

Ilya Grigorik

22,961 views • 9 months ago

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

32,315 views • 9 months ago

This is actually insane 😭 This AI slop generated 6.2M views across 19 posts Literally 1 of the easiest ways to speedrun $10k/mo with your app They convert viewers into users by prompting them to comment “Bible” Then they have an automation to send the app link to the ones who comment. AI slop doesn’t need to be perfect It just needs to be posted And you can easily automate content this, just read the article below

This is actually insane 😭 This AI slop generated 6.2M views across 19 posts Literally 1 of the easiest ways to speedrun $10k/mo with your app They convert viewers into users by prompting them to comment “Bible” Then they have an automation to send the app link to the ones who comment. AI slop doesn’t need to be perfect It just needs to be posted And you can easily automate content this, just read the article below

Ernesto Lopez

186,088 views • 3 months ago

🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.

🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.

OpenServ

34,665 views • 1 year ago

New upgrades to the Google Gemini are you helping you get more done: ✨Gemini Spark is your 24/7 personal AI agent that can take action on your behalf, under your direction. It seamlessly integrates with Gmail, Google Docs, and Slides to automate your workflows and, best of all, it can keep working even when your laptop is closed. ☀ ️Daily Brief is our newest out-of-the-box agent that gives you a personalized digest based on your goals, and suggests next steps. Daily Brief is rolling out starting today to all Google AI subscribers (18+) in the Gemini app, starting in the US. Gemini Spark is starting to roll out next week.

New upgrades to the Google Gemini are you helping you get more done: ✨Gemini Spark is your 24/7 personal AI agent that can take action on your behalf, under your direction. It seamlessly integrates with Gmail, Google Docs, and Slides to automate your workflows and, best of all, it can keep working even when your laptop is closed. ☀ ️Daily Brief is our newest out-of-the-box agent that gives you a personalized digest based on your goals, and suggests next steps. Daily Brief is rolling out starting today to all Google AI subscribers (18+) in the Gemini app, starting in the US. Gemini Spark is starting to roll out next week.

Google AI

39,138 views • 2 months ago

Introducing Deep Graph Codebase to MCP with One Click... Now Available for Gemini CLI too! 🤩 Transform any public GitHub repository into an AI-ready Code Graph by simply changing the URL from to This instantly prepares the repository for seamless integration with AI assistants. It's that easy! You can now use repositories like ollama as a code graph within any project using Gemini CLI... just add the MCP Server JSON configuration to your .gemini/settings.json file

Introducing Deep Graph Codebase to MCP with One Click... Now Available for Gemini CLI too! 🤩 Transform any public GitHub repository into an AI-ready Code Graph by simply changing the URL from to This instantly prepares the repository for seamless integration with AI assistants. It's that easy! You can now use repositories like ollama as a code graph within any project using Gemini CLI... just add the MCP Server JSON configuration to your .gemini/settings.json file

Daniel San

53,516 views • 1 year ago

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!

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!

Andrew Ng

142,010 views • 1 year ago

The future of writing database queries is not writing them at all! MongoDB just released their own MCP Server, and it quietly shifts how we interact with databases. It connects to AI tools like Claude, Cursor, or GitHub Copilot, and you can just talk to your database using natural language: → "What's the schema for the orders collection?" → "Find the top 10 users by purchase volume." → "Create a read-only DB user." No need to context switch. No need to memorize query syntax. No need to even leave your IDE. It works across MongoDB Atlas, Community, and Enterprise, and supports admin ops, schema exploration, indexing, and code generation, all via natural language. This way, you can focus on what actually matters: data modeling, app logic, and product experience. Link to the announcement in the next tweet. Thanks to #MongoDB team for partnering with me today!

The future of writing database queries is not writing them at all! MongoDB just released their own MCP Server, and it quietly shifts how we interact with databases. It connects to AI tools like Claude, Cursor, or GitHub Copilot, and you can just talk to your database using natural language: → "What's the schema for the orders collection?" → "Find the top 10 users by purchase volume." → "Create a read-only DB user." No need to context switch. No need to memorize query syntax. No need to even leave your IDE. It works across MongoDB Atlas, Community, and Enterprise, and supports admin ops, schema exploration, indexing, and code generation, all via natural language. This way, you can focus on what actually matters: data modeling, app logic, and product experience. Link to the announcement in the next tweet. Thanks to #MongoDB team for partnering with me today!

Akshay 🚀

32,084 views • 11 months ago