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The easiest way to build an MCP Server using Google DeepMind Gemini 2.5 Pro and get started! 1. Use Gitingest to get all the code and docs from the FastMCP repo 2. Download the code into a txt file 3. Go to AI Studio, upload the file, define what... show more

Philipp Schmid

88,551 subscribers

75,968 просмотров • 1 год назад •via X (Twitter)

Новости и политика Наука и технологии Образование

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Комментарии: 9

Фото профиля PDF GPT

PDF GPT1 год назад

Everyone is getting ahead with AI. You should be too. Summarize documents, craft emails, and generate custom content instantly with this powerful tool. It's like having ChatGPT tailored for your job. Try it for free.

Фото профиля Markus Odenthal

Markus Odenthal1 год назад

@GoogleDeepMind Nice. This is how I build all my projects. I have a folder called ai_docs inside my projects. That’s where I place documentation or examples of what I want to implement. It works pretty well for me too. LLMs just need the right context and pattern.

Фото профиля Liad Yosef

Liad Yosef1 год назад

@GoogleDeepMind Why use gitingest when you can directly use GitMCP for documentation? (specifically for FastMCP)

Фото профиля Sam Woods

Sam Woods1 год назад

@GoogleDeepMind Gemini 2.5 just turned server setup into a weekend project

Фото профиля Oblix

Oblix1 год назад

Wild how easy this flow is 👇 Gemini 2.5 Pro went from "huh?" to "hold my coffee" real quick. Tried this with a custom routing layer and it nailed the scaffolding + infra logic. Definitely worth experimenting with if you’ve written off Gemini before. #AItools #Gemini #DevTools #FastMCP

Фото профиля Sagar Patil

Sagar Patil1 год назад

@GoogleDeepMind When can we expect MCP integration with AI Studio/Gemini web app?

Фото профиля Lunaris

Lunaris1 год назад

@GoogleDeepMind Woah! This is crazy usecase you have mentioned 🔥

Фото профиля Adam BEN KHALIFA

Adam BEN KHALIFA1 год назад

@GoogleDeepMind This is the best post I saw today

Фото профиля literally_vibing💫

literally_vibing💫1 год назад

@GoogleDeepMind That's cool

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The plot thickens. 🕵️ Use Gemini 2.5 Pro in the Google Gemini to turn random videos from your camera roll into a dramatic narrative. Try it yourself: 1️⃣ Go to Gemini and select “2.5 Pro” from the model dropdown. 2️⃣ Tap the plus icon and upload three videos from your camera roll. (Pro/Ultra subscribers, try using your Veo creations! 🎥) 3️⃣ Ask Gemini to write a short story based on the videos. Here’s our prompt to get you started: “Come up with a premise for an epic adventure based on these videos.”

The plot thickens. 🕵️ Use Gemini 2.5 Pro in the Google Gemini to turn random videos from your camera roll into a dramatic narrative. Try it yourself: 1️⃣ Go to Gemini and select “2.5 Pro” from the model dropdown. 2️⃣ Tap the plus icon and upload three videos from your camera roll. (Pro/Ultra subscribers, try using your Veo creations! 🎥) 3️⃣ Ask Gemini to write a short story based on the videos. Here’s our prompt to get you started: “Come up with a premise for an epic adventure based on these videos.”

Google

164,412 просмотров • 1 год 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:

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

81,563 просмотров • 9 месяцев назад

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 просмотров • 1 год назад

That is the easiest way to chat with a complete Github Repository!👀 Replace "github" with "gitingest" in the url, and you get the whole repo as a single string, paste into AI studio and use Google DeepMind Gemini 2.0 Flash Million Token Context video to ask questions! 🤯 Here is how you can chat with the whole python SDK docs. ⬇️

That is the easiest way to chat with a complete Github Repository!👀 Replace "github" with "gitingest" in the url, and you get the whole repo as a single string, paste into AI studio and use Google DeepMind Gemini 2.0 Flash Million Token Context video to ask questions! 🤯 Here is how you can chat with the whole python SDK docs. ⬇️

Philipp Schmid

97,475 просмотров • 1 год назад

Build your first AI agent + MCP Server in Python. Here is everything you need to build your first AI agent in less than 20 minutes. About the code you'll see here: 1. I used Google ADK with Gemini Flash to power the agent 2. The agent connects to an MCP server 3. It also uses two custom tools to do its work 4. You can see everything the agent does thanks to Comet's Opik library Here is the video, free for you to watch.

Build your first AI agent + MCP Server in Python. Here is everything you need to build your first AI agent in less than 20 minutes. About the code you'll see here: 1. I used Google ADK with Gemini Flash to power the agent 2. The agent connects to an MCP server 3. It also uses two custom tools to do its work 4. You can see everything the agent does thanks to Comet's Opik library Here is the video, free for you to watch.

Santiago

56,569 просмотров • 1 год назад

Here's how to build an MCP server with a single TypeScript file, and connect it to Claude Code. No build step. No bullshit. Totally transformative.

Here's how to build an MCP server with a single TypeScript file, and connect it to Claude Code. No build step. No bullshit. Totally transformative.

Matt Pocock

254,095 просмотров • 1 год назад

The pace at which Gemini 2.5 Pro is improving in code is wild! I generally leaned towards using Claude for code, but now I rely more on Gemini 2.5 Pro. It's just that good! Watch me use Gemini-2.5-Pro-05-06 to go from a YT video to an interactive slide deck app in minutes!

The pace at which Gemini 2.5 Pro is improving in code is wild! I generally leaned towards using Claude for code, but now I rely more on Gemini 2.5 Pro. It's just that good! Watch me use Gemini-2.5-Pro-05-06 to go from a YT video to an interactive slide deck app in minutes!

elvis

81,882 просмотров • 1 год назад

MCP broke my brain a little because it rethinks the entire workflow. Debug, diagnose and file the issue with Playwright and GitHub MCP. All from agent mode in Visual Studio Code. What are you using MCP for beyond code?

MCP broke my brain a little because it rethinks the entire workflow. Debug, diagnose and file the issue with Playwright and GitHub MCP. All from agent mode in Visual Studio Code. What are you using MCP for beyond code?

Burke Holland

34,733 просмотров • 1 год 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!

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 просмотров • 1 год назад

For developers, Gemini 3 Pro offers unparalleled results across every major AI benchmark compared to Gemini 2.5 Pro. Here three ways Gemini 3 Pro can improve your workflow today: 1. Gemini 3 Pro fits right into existing production agent and coding workflows. It is also integrated into development environment experiences across the ecosystem of developer products like Google AI Studio, Gemini CLI, and Android Studio. 2. We introduced Google Google Antigravity, a new agentic development platform that reimagines how the model and IDE work together. It enables developers to manage agents to work at a higher, task-oriented level collaborating with agents while they build. 3. We’ve built Google AI Studio to be your fastest path from a prompt to an AI-native app. Build mode lets you add AI capabilities faster than ever, automatically wiring up the right models and APIs, while features like annotations enable fast and intuitive iteration. Whether you’re building a one-shot prompt game or an interactive landing page from unstructured voice notes, you can bring any idea to life with Gemini 3.

For developers, Gemini 3 Pro offers unparalleled results across every major AI benchmark compared to Gemini 2.5 Pro. Here three ways Gemini 3 Pro can improve your workflow today: 1. Gemini 3 Pro fits right into existing production agent and coding workflows. It is also integrated into development environment experiences across the ecosystem of developer products like Google AI Studio, Gemini CLI, and Android Studio. 2. We introduced Google Google Antigravity, a new agentic development platform that reimagines how the model and IDE work together. It enables developers to manage agents to work at a higher, task-oriented level collaborating with agents while they build. 3. We’ve built Google AI Studio to be your fastest path from a prompt to an AI-native app. Build mode lets you add AI capabilities faster than ever, automatically wiring up the right models and APIs, while features like annotations enable fast and intuitive iteration. Whether you’re building a one-shot prompt game or an interactive landing page from unstructured voice notes, you can bring any idea to life with Gemini 3.

Google AI

136,589 просмотров • 7 месяцев назад

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:

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 просмотров • 1 год назад

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 просмотров • 1 год назад

You can team Gemini 2.5 Pro with OpenAI's o3 model to build beautiful SwiftUI animations. This example prompts Gemini 2.5 Pro to watch an mp4 video of a recorded animation and generate a SwiftUI version. Gemini 2.5 Pro isn't great at fixing SwiftUI errors, so we bring o3 to fix all errors using ChatGPT Chat Bar. I will share the gist in a comment.

You can team Gemini 2.5 Pro with OpenAI's o3 model to build beautiful SwiftUI animations. This example prompts Gemini 2.5 Pro to watch an mp4 video of a recorded animation and generate a SwiftUI version. Gemini 2.5 Pro isn't great at fixing SwiftUI errors, so we bring o3 to fix all errors using ChatGPT Chat Bar. I will share the gist in a comment.

Amos Gyamfi

18,408 просмотров • 1 год назад

🎹 Build a music learning app with a single prompt using Gemini 2.5 Pro in Google AI Studio →

🎹 Build a music learning app with a single prompt using Gemini 2.5 Pro in Google AI Studio →

Google AI Developers

65,632 просмотров • 1 год назад

Claude Code performs a complete analysis of Gemini-CLI and OpenAI Codex using Deep Graph MCP... one command to implement Claude Code automatically pulls two popular AI assistant repos and performs deep architectural analysis to determine which one is actually better built. Single command to implement: claude mcp add "Deep Graph MCP" npx -- -y mcp-code-graph@latest google-gemini/gemini-cli openai/codex The results might surprise you 👇

Claude Code performs a complete analysis of Gemini-CLI and OpenAI Codex using Deep Graph MCP... one command to implement Claude Code automatically pulls two popular AI assistant repos and performs deep architectural analysis to determine which one is actually better built. Single command to implement: claude mcp add "Deep Graph MCP" npx -- -y mcp-code-graph@latest google-gemini/gemini-cli openai/codex The results might surprise you 👇

Daniel San

68,001 просмотров • 1 год назад

Write 100x better code using AI (for free!) Install the Codacy extension (VSCode, Cursor, WindSurf). This will install an MCP Server locally on your computer. Your coding copilot will use this server with the Codacy CLI to check every line of code it generates. The way this works is pretty awesome (assuming you are using Cursor): 1. You ask Cursor to generate some code 2. Cursor writes the code 3. Cursor then calls Codacy's MCP Server to check it 4. The MCP Server will use the Codacy CLI to find any issues 5. Cursor will fix the issues and call the MCP server again 6. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Codacy has been doing this for a long, long time, and their Guardrails MCP Server checks for a lot of potential issues: • Security issues • Quality standards • Pattern violations • Linting issues You can see more information about the extension here: I recorded a quick video to show you how it works. Thanks to the Codacy team for helping me set up everything, answering my 1,000 questions, and collaborating with me on this post.

Write 100x better code using AI (for free!) Install the Codacy extension (VSCode, Cursor, WindSurf). This will install an MCP Server locally on your computer. Your coding copilot will use this server with the Codacy CLI to check every line of code it generates. The way this works is pretty awesome (assuming you are using Cursor): 1. You ask Cursor to generate some code 2. Cursor writes the code 3. Cursor then calls Codacy's MCP Server to check it 4. The MCP Server will use the Codacy CLI to find any issues 5. Cursor will fix the issues and call the MCP server again 6. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Codacy has been doing this for a long, long time, and their Guardrails MCP Server checks for a lot of potential issues: • Security issues • Quality standards • Pattern violations • Linting issues You can see more information about the extension here: I recorded a quick video to show you how it works. Thanks to the Codacy team for helping me set up everything, answering my 1,000 questions, and collaborating with me on this post.

Santiago

63,240 просмотров • 1 год назад

I'm still picking my jaw off the floor with this MCP update: Postman will now automatically build an MCP server for an API. Automatically! You select your API and the requests you want to include, and Potman will generate a Zip file with everything you need. In collaboration with Postman, I recorded a video showing how I generated an MCP server for the Google Maps API. It's glorious! Also, and before I forget: You can now test any MCP server with your Postman application. They built MCP right in, so now you can: • Connect to an MCP server • List out all of the available tools • Test any of the tools This is a 10x boost for the MCP ecosystem. If you've tried the default MCP debugger, you'll understand how huge a deal it is that you can do all of that from Postman now. Go here to give this a try:

I'm still picking my jaw off the floor with this MCP update: Postman will now automatically build an MCP server for an API. Automatically! You select your API and the requests you want to include, and Potman will generate a Zip file with everything you need. In collaboration with Postman, I recorded a video showing how I generated an MCP server for the Google Maps API. It's glorious! Also, and before I forget: You can now test any MCP server with your Postman application. They built MCP right in, so now you can: • Connect to an MCP server • List out all of the available tools • Test any of the tools This is a 10x boost for the MCP ecosystem. If you've tried the default MCP debugger, you'll understand how huge a deal it is that you can do all of that from Postman now. Go here to give this a try:

Santiago

215,727 просмотров • 1 год назад

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.

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.

Prajwal Tomar

19,085 просмотров • 9 месяцев назад

Introducing Gemini 3 ✨ It’s the best model in the world for multimodal understanding, and our most powerful agentic + vibe coding model yet. Gemini 3 can bring any idea to life, quickly grasping context and intent so you can get what you need with less prompting. Find Gemini 3 Pro rolling out today in the Google Gemini and AI Mode in Search. For developers, build with it now in Google AI Studio and Vertex AI. Excited for you to try it!

Introducing Gemini 3 ✨ It’s the best model in the world for multimodal understanding, and our most powerful agentic + vibe coding model yet. Gemini 3 can bring any idea to life, quickly grasping context and intent so you can get what you need with less prompting. Find Gemini 3 Pro rolling out today in the Google Gemini and AI Mode in Search. For developers, build with it now in Google AI Studio and Vertex AI. Excited for you to try it!

Sundar Pichai

2,949,695 просмотров • 7 месяцев назад