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Model Context Protocol (MCP) lets Gemini CLI connect to your databases, cloud services, and local files without custom integrations for every tool! Watch this week’s livestream to see how it works →

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Google Cloud Tech

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28,269 просмотров • 3 месяцев назад •via X (Twitter)

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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:
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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

124,181 просмотров • 4 месяцев назад

Gemini CLI now integrates with FastMCP, Python's leading library for building MCP servers! Effortlessly connect your custom MCP tools and prompts, directly to Gemini CLI! Read the announcement→
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Gemini CLI now integrates with FastMCP, Python's leading library for building MCP servers! Effortlessly connect your custom MCP tools and prompts, directly to Gemini CLI! Read the announcement→

Google Cloud Tech

29,477 просмотров • 8 месяцев назад

Today we're also rolling out Model Context Protocol (MCP) inside Deepwork. Proxy already lets anyone automate the web; MCP takes it further - connecting Deepwork to Linear, Asana, Sentry and Intercom, with more integrations coming soon.
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Today we're also rolling out Model Context Protocol (MCP) inside Deepwork. Proxy already lets anyone automate the web; MCP takes it further - connecting Deepwork to Linear, Asana, Sentry and Intercom, with more integrations coming soon.

Convergence

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Explore the dart_mcp package, a powerful framework for building Model Context Protocol (MCP) servers and clients in Dart, enabling you to connect AI models to resources, tools, and create custom workflows.
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Explore the dart_mcp package, a powerful framework for building Model Context Protocol (MCP) servers and clients in Dart, enabling you to connect AI models to resources, tools, and create custom workflows.

Flutter

23,056 просмотров • 1 месяц назад

it really doesnt matter if your agent uses the cli or mcp what really matters is how to best enable ur agents to build autonomously for u without the crossmint cli tool my agent would have to use the browser and get my input in every step. now i just let it run see how i do it
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it really doesnt matter if your agent uses the cli or mcp what really matters is how to best enable ur agents to build autonomously for u without the crossmint cli tool my agent would have to use the browser and get my input in every step. now i just let it run see how i do it

joyce

14,133 просмотров • 3 месяцев назад

Set up your new GitHub Copilot agent for success! 🚀 This video will show you how to: ✅ Define clear, well-scoped issues. ⚙️ Utilize Copilot custom instructions effectively. 💻 Ensure a proper development environment setup. 🔗 Grant tool access via the Model Context Protocol (MCP). Watch and then head over to Docs for more tips! 👇
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Set up your new GitHub Copilot agent for success! 🚀 This video will show you how to: ✅ Define clear, well-scoped issues. ⚙️ Utilize Copilot custom instructions effectively. 💻 Ensure a proper development environment setup. 🔗 Grant tool access via the Model Context Protocol (MCP). Watch and then head over to Docs for more tips! 👇

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69,792 просмотров • 1 год назад

ICYMI: You can now connect Model Context Protocol (MCP) tools into Amazon Q Developer CLI using an expansive list of AWS pre-built integrations or any MCP servers that support stdio. 🖇️🔌💻 Once connected, Q Developer will provide even more context-aware responses, understand data structures and execute complex queries, without needing to write additional integration code. Get started on our expansive free tier today. 👉
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ICYMI: You can now connect Model Context Protocol (MCP) tools into Amazon Q Developer CLI using an expansive list of AWS pre-built integrations or any MCP servers that support stdio. 🖇️🔌💻 Once connected, Q Developer will provide even more context-aware responses, understand data structures and execute complex queries, without needing to write additional integration code. Get started on our expansive free tier today. 👉

Amazon Web Services

17,048 просмотров • 1 год назад

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.
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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 просмотров • 11 месяцев назад

🔥 Contextus is LIVE and it's on FIRE! We're thrilled to drop the latest GitHub update to Contextus, the ultimate Model Context Protocol Manager! ✅ Contextus is an open source service and a CLI package management tool for MCP servers. Stay Tuned!..
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🔥 Contextus is LIVE and it's on FIRE! We're thrilled to drop the latest GitHub update to Contextus, the ultimate Model Context Protocol Manager! ✅ Contextus is an open source service and a CLI package management tool for MCP servers. Stay Tuned!..

Matrix AI Network

14,010 просмотров • 1 год назад

Gemini CLI can automate your computer using MCP 🔥 Add Windows MCP (or macOS MCP) to Gemini CLI and you can tell it what to do autonomously. Gemini then takes control of your entire system to achieve the goal you've set. Links below
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Gemini CLI can automate your computer using MCP 🔥 Add Windows MCP (or macOS MCP) to Gemini CLI and you can tell it what to do autonomously. Gemini then takes control of your entire system to achieve the goal you've set. Links below

Paul Couvert

131,661 просмотров • 11 месяцев назад

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:
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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

80,860 просмотров • 8 месяцев назад

Think of Model Context Protocol as a USB port for your AI. 🔌 You can plug in documentation, tools, and context directly into Copilot CLI. Scott Hanselman 🌮 shows how to set it all up. 👇
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Think of Model Context Protocol as a USB port for your AI. 🔌 You can plug in documentation, tools, and context directly into Copilot CLI. Scott Hanselman 🌮 shows how to set it all up. 👇

GitHub

44,242 просмотров • 4 месяцев назад

👋 Meet the Model Context Protocol (MCP): the open standard allowing apps to feed vital context to LLMs. Why is this protocol so important? Because context is what turns those generic AI replies into relevant, useful assistance tailored to your work. In this video, ☁️ Chris Reddington explains how MCP delivers this crucial info and why it makes such a difference for AI pair programmers like GitHub Copilot. 💡
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👋 Meet the Model Context Protocol (MCP): the open standard allowing apps to feed vital context to LLMs. Why is this protocol so important? Because context is what turns those generic AI replies into relevant, useful assistance tailored to your work. In this video, ☁️ Chris Reddington explains how MCP delivers this crucial info and why it makes such a difference for AI pair programmers like GitHub Copilot. 💡

GitHub

35,316 просмотров • 1 год назад

Introducing Gemini CLI extensions, a new framework that allows you to customize Gemini CLI and connect it to the tools you use most—all from the command line. No more context-switching. Start using your favorite tools directly in your workflow today →
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Introducing Gemini CLI extensions, a new framework that allows you to customize Gemini CLI and connect it to the tools you use most—all from the command line. No more context-switching. Start using your favorite tools directly in your workflow today →

Google Cloud Tech

80,461 просмотров • 7 месяцев назад

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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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

141,930 просмотров • 1 год назад

Big launch today: Custom MCP integrations for Replit Agent! 🚀 You can now connect Replit Agent to remote MCP servers, letting it pull context and take actions across your tools- so it can build, update, and act with deeper context than ever. Use cases this unlocks for you: - Build a webpage from content stored in Notion - Update tasks in Linear/Jira automatically - Build an app based on content in your Miro board We can’t wait to see what you build with it. ✨
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Big launch today: Custom MCP integrations for Replit Agent! 🚀 You can now connect Replit Agent to remote MCP servers, letting it pull context and take actions across your tools- so it can build, update, and act with deeper context than ever. Use cases this unlocks for you: - Build a webpage from content stored in Notion - Update tasks in Linear/Jira automatically - Build an app based on content in your Miro board We can’t wait to see what you build with it. ✨

Replit ⠕

53,075 просмотров • 5 месяцев назад

Anthropic Model Context Protocol Demo! 📚 Step-by-step Hands-on Tutorial 🛠️ Custom Tools, Custom Actions in Anthropic 🔗 Add YOUR App to Claude AI! ⚙️ What is MCP? ⚡ How to Install? 🌦️ How to Integrate Custom Weather App? 🏃‍♂️ How to Create Notes App? 🔌 How to Integrate in Desktop App? Follow Mervin Praison for more
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Anthropic Model Context Protocol Demo! 📚 Step-by-step Hands-on Tutorial 🛠️ Custom Tools, Custom Actions in Anthropic 🔗 Add YOUR App to Claude AI! ⚙️ What is MCP? ⚡ How to Install? 🌦️ How to Integrate Custom Weather App? 🏃‍♂️ How to Create Notes App? 🔌 How to Integrate in Desktop App? Follow Mervin Praison for more

Mervin Praison

22,200 просмотров • 1 год назад

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇
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We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇

Taranjeet

471,718 просмотров • 1 год назад

Gemini CLI can now stop and ask for clarification using interactive dialogues, multiple-choice, and yes/no questions before it builds. See the brand-new Ask Me feature in action on this week’s livestream →
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Gemini CLI can now stop and ask for clarification using interactive dialogues, multiple-choice, and yes/no questions before it builds. See the brand-new Ask Me feature in action on this week’s livestream →

Google Cloud Tech

19,883 просмотров • 3 месяцев назад

Totally settling the "MCP vs CLI for coding agents" debate with this humongous blog post. I benchmarked screen, tmux, and a bespoke custom CLI and MCP tool against 3 task that require the agent to steer TUIs (lldb, python repl, opencode)
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Totally settling the "MCP vs CLI for coding agents" debate with this humongous blog post. I benchmarked screen, tmux, and a bespoke custom CLI and MCP tool against 3 task that require the agent to steer TUIs (lldb, python repl, opencode)

Mario Zechner

63,924 просмотров • 9 месяцев назад