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Discover the power of Firebase's MCP Server! 💪 This experimental tool streamlines communication and helps enable secure AI actions on behalf of users. Chapters: 0:00 - Introduction 1:02 - Set up an MCP Server 1:41 - Current Project Demo 2:48 - Data Connect Demo 3:35 - Setting an SMS... show more

Firebase

209,325 subscribers

14,336 views • 1 year ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

4 Comments

The Martian1 year ago

Hurray! Getting right on it

Wonderchat2 years ago

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J Sam🌐1 year ago

ok

❝Rijn Hartman❞1 year ago

so what happened last night with all the cloud servers being down? we just ignore that it happened? 👀

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All the updates from Firebase at #GoogleIO in one video! Watch and learn 👇 Chapters: 0:00 - Firebase Studio updates 1:18 - More ways to deploy App Hosting 2:38 - Meet Firebase AI Logic 4:54 - Firebase MCP Server 5:29 - Data Connect updates

All the updates from Firebase at #GoogleIO in one video! Watch and learn 👇 Chapters: 0:00 - Firebase Studio updates 1:18 - More ways to deploy App Hosting 2:38 - Meet Firebase AI Logic 4:54 - Firebase MCP Server 5:29 - Data Connect updates

Firebase

36,809 views • 1 year ago

🤖 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 views • 1 year ago

MCP is an absolute game-changer. Yesterday, I taught a 2-hour session about MCP covering the following topics: 1. Introduction to the protocol 2. Why do we need it? 3. Protocol architecture 4. MCP Server and Client primitives 5. An MCP demo Here is the full 2-hour video, free for you to watch. If you want to see more free sessions like this, like this post and follow me. A lot more coming!

MCP is an absolute game-changer. Yesterday, I taught a 2-hour session about MCP covering the following topics: 1. Introduction to the protocol 2. Why do we need it? 3. Protocol architecture 4. MCP Server and Client primitives 5. An MCP demo Here is the full 2-hour video, free for you to watch. If you want to see more free sessions like this, like this post and follow me. A lot more coming!

Santiago

36,265 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

Catch the first Firebase Release Notes of 2026! 🥳 We're covering updates to Firebase AI Logic, the Firebase CLI, and more. Chapters: 0:00 - Building with Firebase AI Logic 1:03 - Server prompt templates 1:36 - Backend & database updates 2:28 - CLI and tools

Catch the first Firebase Release Notes of 2026! 🥳 We're covering updates to Firebase AI Logic, the Firebase CLI, and more. Chapters: 0:00 - Building with Firebase AI Logic 1:03 - Server prompt templates 1:36 - Backend & database updates 2:28 - CLI and tools

Firebase

16,921 views • 5 months ago

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 views • 1 year ago

Ready to get started with @Antigravity? You can connect the Firebase MCP server to take your app development to the next level 🚀 Learn how to add Firebase MCP to Antigravity, link your current project, and start building →

Ready to get started with @Antigravity? You can connect the Firebase MCP server to take your app development to the next level 🚀 Learn how to add Firebase MCP to Antigravity, link your current project, and start building →

Firebase

91,039 views • 7 months ago

Introducing Document Extraction as an MCP Server ✂️📑 A huge use case for AI agents is being able to extract out items from a diverse set of complex documents in a repeatable manner - whether it’s legal contracts, invoices, financial statements, passports, and more. In this case, 🚫 prompting ChatGPT/Claude is too error-prone/not systematic. 💡We’ve created a plug-and-play MCP server around our core document extraction capabilities within LlamaCloud. 1. Build an extraction agent in LlamaCloud with a standardized schema. 2. Plug it as an MCP tool within ChatGPT/Claude. 3. For your next extraction job, get the outputs in a standardized format through the tool! Huge shoutout to Tuana and Logan Markewich for working on this - this is thanks to the latest updates to our llamacloud-mcp demo repo, which supports both extraction and retrieval as MCP tools. Repo: Sign up:

Introducing Document Extraction as an MCP Server ✂️📑 A huge use case for AI agents is being able to extract out items from a diverse set of complex documents in a repeatable manner - whether it’s legal contracts, invoices, financial statements, passports, and more. In this case, 🚫 prompting ChatGPT/Claude is too error-prone/not systematic. 💡We’ve created a plug-and-play MCP server around our core document extraction capabilities within LlamaCloud. 1. Build an extraction agent in LlamaCloud with a standardized schema. 2. Plug it as an MCP tool within ChatGPT/Claude. 3. For your next extraction job, get the outputs in a standardized format through the tool! Huge shoutout to Tuana and Logan Markewich for working on this - this is thanks to the latest updates to our llamacloud-mcp demo repo, which supports both extraction and retrieval as MCP tools. Repo: Sign up:

Jerry Liu

57,827 views • 1 year ago

Hot off the presses! 📰🔥 Firebase Release Notes is here with the latest news from Firebase. Chapters: 0:00 - Firestore updates 1:00 - Data Connect is now SQL Connect 1:47 - Experimental support for Dart on Firebase Functions 2:01 - Firebase AI Logic updates 2:33 - Firebase Authentication Phone Number Verification 3:09 - Firebase integration with Application Design Center

Hot off the presses! 📰🔥 Firebase Release Notes is here with the latest news from Firebase. Chapters: 0:00 - Firestore updates 1:00 - Data Connect is now SQL Connect 1:47 - Experimental support for Dart on Firebase Functions 2:01 - Firebase AI Logic updates 2:33 - Firebase Authentication Phone Number Verification 3:09 - Firebase integration with Application Design Center

Firebase

15,120 views • 2 months ago

One of the biggest headaches in mobile AI is deciding between a cloud model (reliable but costly) and an on-device model (fast but fragmented) 😵‍💫 With hybrid inference, you don't have to choose! Learn the details in this deep dive. Chapters: 0:00 - Introducing hybrid SDKs for Firebase AI Logic 0:41 - Use Case: AI-Powered star ratings & reviews 1:00 - Why local? Reliability, offline support, & cost 1:30 - Coding step 1: SDKs and AICore setup 2:10 - Fallback strategies & JsonSchema 2:52 - Demo: Real-time hybrid inference in action

One of the biggest headaches in mobile AI is deciding between a cloud model (reliable but costly) and an on-device model (fast but fragmented) 😵‍💫 With hybrid inference, you don't have to choose! Learn the details in this deep dive. Chapters: 0:00 - Introducing hybrid SDKs for Firebase AI Logic 0:41 - Use Case: AI-Powered star ratings & reviews 1:00 - Why local? Reliability, offline support, & cost 1:30 - Coding step 1: SDKs and AICore setup 2:10 - Fallback strategies & JsonSchema 2:52 - Demo: Real-time hybrid inference in action

Firebase

11,979 views • 2 months ago

You need 6 key skills to become an AI PM. Here's a demo of all 6 with Paweł Huryn: 1. Prompting - 02:39 2. AI PRDs - 11:54 3. Fine-Tuning - 16:42 4. RAG - 30:03 5. MCP - 59:00 6. AI Agents - 01:18:31

You need 6 key skills to become an AI PM. Here's a demo of all 6 with Paweł Huryn: 1. Prompting - 02:39 2. AI PRDs - 11:54 3. Fine-Tuning - 16:42 4. RAG - 30:03 5. MCP - 59:00 6. AI Agents - 01:18:31

Aakash Gupta

110,146 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

In this video, we break down the Model Context Protocol — a massively underrated concept in AI that's quietly redefining what large language models can do. While most people are distracted by the hype, MCP is solving a core problem: LLMs that can perform actions. We dive into: 1. The limitations of current AI systems 2. What makes MCP different 3. How MCP can reduce engineering overhead 4. Real-world scenarios where MCP shines 00:00 What is MCP? 01:48 Usecase - SEO 03:50 Usecase - RAG 04:50 Usecase - Apps 06:30 Conclusion 07:00 The future? #MCP #AI #LLM

In this video, we break down the Model Context Protocol — a massively underrated concept in AI that's quietly redefining what large language models can do. While most people are distracted by the hype, MCP is solving a core problem: LLMs that can perform actions. We dive into: 1. The limitations of current AI systems 2. What makes MCP different 3. How MCP can reduce engineering overhead 4. Real-world scenarios where MCP shines 00:00 What is MCP? 01:48 Usecase - SEO 03:50 Usecase - RAG 04:50 Usecase - Apps 06:30 Conclusion 07:00 The future? #MCP #AI #LLM

Gaurav Sen

41,117 views • 1 year ago

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 kind of MCP Server you want to build 4. Gemini 2.5 Pro builds it for you.

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 kind of MCP Server you want to build 4. Gemini 2.5 Pro builds it for you.

Philipp Schmid

75,968 views • 1 year ago

GPT-Realtime 2 is the future of the operating system. I've been experimenting with it for a couple weeks now, and I gotta say, it's pretty gosh darn incredible. Opening apps, searching the web, even editing in Premiere. All with just my voice. And it only takes a few prompts to set up. In this video I'll show you exactly how. 0:00 Intro 1:46 What is GPT-Realtime 2? 4:46 Setting it up (no coding) 7:53 Fixing the always-on mic (push-to-talk) 9:41 Demo: searching the web 11:38 Demo: connecting apps via MCP (Obsidian) 14:13 Demo: controlling Premiere Pro (accessibility tree) 18:00 Honest caveats 19:00 Outro

GPT-Realtime 2 is the future of the operating system. I've been experimenting with it for a couple weeks now, and I gotta say, it's pretty gosh darn incredible. Opening apps, searching the web, even editing in Premiere. All with just my voice. And it only takes a few prompts to set up. In this video I'll show you exactly how. 0:00 Intro 1:46 What is GPT-Realtime 2? 4:46 Setting it up (no coding) 7:53 Fixing the always-on mic (push-to-talk) 9:41 Demo: searching the web 11:38 Demo: connecting apps via MCP (Obsidian) 14:13 Demo: controlling Premiere Pro (accessibility tree) 18:00 Honest caveats 19:00 Outro

Pat Simmons

601,784 views • 29 days ago

How to access a TREASURE TROVE of local business data with Gemini's new Google Maps tool (10 min tutorial) 1) build an MCP server 2) find boring business ideas 3) generate full on marketing/sales campaigns Plus ideas for taking this to the next level if you want to build it

How to access a TREASURE TROVE of local business data with Gemini's new Google Maps tool (10 min tutorial) 1) build an MCP server 2) find boring business ideas 3) generate full on marketing/sales campaigns Plus ideas for taking this to the next level if you want to build it

The Boring Marketer

36,491 views • 8 months ago

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10

Dan Shipper 📧

15,645 views • 9 months ago

MCP now supports authentication using OAuth2. This is huge! I want to share a complete example (code) so you can learn how to use it. Here is the thing: You don't want to build agentic applications that access resources without proper guardrails. With authentication, you can: • Call third-party APIs on the user's behalf • Perform actions that should be traceable • Enforce different permissions for different roles Here is an MCP-based web shop demo, built with WorkOS AuthKit and Next.js. With this example, you can do the following: • Connect to an MCP server • Authenticate using AuthKit • Order a T-shirt using MCP tools • View order details if you are an admin This GitHub repository contains everything you need: The WorkOS team has one of the best authentication platforms in the world. If you haven't looked into AuthKit, take a look at the code of this demo. Thanks to the WorkOS team for sponsoring this post.

MCP now supports authentication using OAuth2. This is huge! I want to share a complete example (code) so you can learn how to use it. Here is the thing: You don't want to build agentic applications that access resources without proper guardrails. With authentication, you can: • Call third-party APIs on the user's behalf • Perform actions that should be traceable • Enforce different permissions for different roles Here is an MCP-based web shop demo, built with WorkOS AuthKit and Next.js. With this example, you can do the following: • Connect to an MCP server • Authenticate using AuthKit • Order a T-shirt using MCP tools • View order details if you are an admin This GitHub repository contains everything you need: The WorkOS team has one of the best authentication platforms in the world. If you haven't looked into AuthKit, take a look at the code of this demo. Thanks to the WorkOS team for sponsoring this post.

Santiago

33,004 views • 7 months ago

🚨 Unity AI is finally opening up, and this is a big one for Unity developers 🚀 You now get access to an in-project agentic assistant, AI Gateway, MCP server support, and deeper editor context so AI tools can understand your scene, assets, packages, hierarchy, settings, and target platforms. This is the kind of workflow I’ve been waiting for, AI that actually understands your Unity project and helps you build faster.

🚨 Unity AI is finally opening up, and this is a big one for Unity developers 🚀 You now get access to an in-project agentic assistant, AI Gateway, MCP server support, and deeper editor context so AI tools can understand your scene, assets, packages, hierarchy, settings, and target platforms. This is the kind of workflow I’ve been waiting for, AI that actually understands your Unity project and helps you build faster.

Dilmer 🔜 XRCC 🇩🇪

17,542 views • 2 months ago

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

63,262 views • 1 year ago