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🚀 Revolutionize your AI workflow with these 3 amazing #MCP servers covered by Erik Hanchett: 🔍 Brave Search: Up to the minute information 📚 AWS Documentation: Instant tech answers 🐙 GitHub: Code analysis & repo management magic Get started now! 👉

AWS Developers

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13,486 views • 1 year ago •via X (Twitter)

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Connecting Claude with web search is magic! Finally got the chance to play with MCP servers. I hooked up Claude Desktop with GitHub & Brave web search MCP servers for staying up to date with AI news. I also asked Claude to create a new GitHub repo and log the news there. This exceeded my expectations. Check the full video (at 2x) for the first demo. Let me know if you are interested in a full tutorial on how to set up the MCP servers.

Connecting Claude with web search is magic! Finally got the chance to play with MCP servers. I hooked up Claude Desktop with GitHub & Brave web search MCP servers for staying up to date with AI news. I also asked Claude to create a new GitHub repo and log the news there. This exceeded my expectations. Check the full video (at 2x) for the first demo. Let me know if you are interested in a full tutorial on how to set up the MCP servers.

elvis

41,804 views • 1 year ago

🚀 Big news: Cline just launched the MCP Marketplace -- now you can one-click install MCP servers and give Cline access to a whole new suite of tools! What's changing with the MCP Marketplace: • Browse AI capabilities like apps • One-click MCP server installation • Automatic setup and configuration • No more JSON wrestling Before: - Find GitHub repos - Read technical docs - Configure manually - Hope it works Now: - Open Cline - Browse marketplace - Click "Install" - Start using The impact: Making AI superpowers accessible to everyone, not just tech experts. Imagine giving your AI instant access to: • Web searching capabilities • Design tools integration • Code analysis powers • And much more For developers: Want to list your MCP server? Create an issue in our mcp-marketplace repo with: • GitHub repo URL • 400x400 PNG logo • Brief description The future of AI development isn't about complex integrations. It's about unlocking capabilities with a single click. Welcome to that future. 🔮

Cline

112,058 views • 1 year ago

X just made itself plug straight into AI tools with MCP servers. Your agents can now search and post on X directly. Not only read about it. Grok Build, Cursor, Claude, VS Code. All in. What you get: * X MCP; search posts, trends, bookmarks, even publish Articles * Docs MCP; API docs inside your workflow, no tab switching Setup is straightforward: * Go to create an X app * Enable OAuth 2.0; required for authentication * Add redirect URI; * Copy your CLIENT_ID and CLIENT_SECRET * Plug those into your MCP client or config * Run the setup commands; connect X MCP and Docs MCP * Log in once via browser; approve access * Verify; run grok mcp doctor xapi and grok mcp list Log in once. Done. Your workflow now includes X itself. X

X just made itself plug straight into AI tools with MCP servers. Your agents can now search and post on X directly. Not only read about it. Grok Build, Cursor, Claude, VS Code. All in. What you get: * X MCP; search posts, trends, bookmarks, even publish Articles * Docs MCP; API docs inside your workflow, no tab switching Setup is straightforward: * Go to create an X app * Enable OAuth 2.0; required for authentication * Add redirect URI; * Copy your CLIENT_ID and CLIENT_SECRET * Plug those into your MCP client or config * Run the setup commands; connect X MCP and Docs MCP * Log in once via browser; approve access * Verify; run grok mcp doctor xapi and grok mcp list Log in once. Done. Your workflow now includes X itself. X

Mario Nawfal

59,976 views • 18 days ago

🚀 Just launched: The GitHub Models repo integration is in public preview. ✨ Now you can: ➡️ Version, review, and collaborate on .prompt.yml files just like code. ➡️ Compare outputs side-by-side from 40+ leading LLMs. ➡️ Easily set up with a single API key for FREE. ➡️ Benefit from enterprise-ready governance and security on GitHub and Azure. Ready to start building efficiently with AI models? Get started. 👇

🚀 Just launched: The GitHub Models repo integration is in public preview. ✨ Now you can: ➡️ Version, review, and collaborate on .prompt.yml files just like code. ➡️ Compare outputs side-by-side from 40+ leading LLMs. ➡️ Easily set up with a single API key for FREE. ➡️ Benefit from enterprise-ready governance and security on GitHub and Azure. Ready to start building efficiently with AI models? Get started. 👇

GitHub

37,934 views • 1 year ago

Agent Workflows are the fastest and most flexible way to build complex agentic systems, and we put them to the test. Our OSS engineer Clelia Bertelli (🦙/acc) built a powerful integration with the Agent Client Protocol (ACP) by Zed, resulting in a fully customizable agent with access to filesystem and bash tools, MCP servers, persistent memory, and built-in TODO tracking 📝 Check out the demo, where the agent explores the LlamaIndex documentation using our dedicated MCP server, or try it yourself with 𝘶𝘷 𝘵𝘰𝘰𝘭 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴-𝘢𝘤𝘱 Get started with LlamaAgents: GitHub repo 👉

Agent Workflows are the fastest and most flexible way to build complex agentic systems, and we put them to the test. Our OSS engineer Clelia Bertelli (🦙/acc) built a powerful integration with the Agent Client Protocol (ACP) by Zed, resulting in a fully customizable agent with access to filesystem and bash tools, MCP servers, persistent memory, and built-in TODO tracking 📝 Check out the demo, where the agent explores the LlamaIndex documentation using our dedicated MCP server, or try it yourself with 𝘶𝘷 𝘵𝘰𝘰𝘭 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴-𝘢𝘤𝘱 Get started with LlamaAgents: GitHub repo 👉

LlamaIndex 🦙

16,097 views • 6 months ago

🚀 Cline 3.4 is here! We've been cooking up something special, and today we're excited to share this huge update! Here's what's new: ⭐️ MCP Marketplace: Install the best MCP servers with just one click 🧜‍♀️ Mermaid Diagrams: Visual flowcharts right in your chat We've also added: • Smarter Context: Real-time terminal awareness + Git integration • New Models: Qwen 2.5, DeepSeek, and enhanced AWS Bedrock support • Advanced Settings: More control over your development environment The best part? It's all designed to make your coding workflow smoother and more intuitive. Get started with Cline 3.4 👇

🚀 Cline 3.4 is here! We've been cooking up something special, and today we're excited to share this huge update! Here's what's new: ⭐️ MCP Marketplace: Install the best MCP servers with just one click 🧜‍♀️ Mermaid Diagrams: Visual flowcharts right in your chat We've also added: • Smarter Context: Real-time terminal awareness + Git integration • New Models: Qwen 2.5, DeepSeek, and enhanced AWS Bedrock support • Advanced Settings: More control over your development environment The best part? It's all designed to make your coding workflow smoother and more intuitive. Get started with Cline 3.4 👇

Cline

109,732 views • 1 year ago

Qwen-Code weekly release (v0.0.8) : ✨ Deep VS Code Integration: Get context-aware suggestions & inline diffs directly in your editor! Initialize with /ide and supercharge your workflow. 🔌 Enhanced MCP Support: Add, remove, list MCP servers via CLI (qwen mcp add|remove|list), plus support for MCP roots & multimodal tool outputs (images, audio, and more!). 📱 Responsive Terminal UI: Now adapts gracefully to narrow terminals. 🔍 Reverse Search: Press Ctrl+R in shell mode to instantly search command history. 🗜️ Customizable Context Compression: Set your threshold in settings.json. 📂 Multi-directory Support: Auto-load multiple dirs at startup. 🔢 Hide Line Numbers: Set "showLineNumbers": false for cleaner copy-paste. 🔕 Plus: Disable update nudges with "disableUpdateNag": true 📌More details: Upgrade now and level up your coding experience 💻🔥

Qwen-Code weekly release (v0.0.8) : ✨ Deep VS Code Integration: Get context-aware suggestions & inline diffs directly in your editor! Initialize with /ide and supercharge your workflow. 🔌 Enhanced MCP Support: Add, remove, list MCP servers via CLI (qwen mcp add|remove|list), plus support for MCP roots & multimodal tool outputs (images, audio, and more!). 📱 Responsive Terminal UI: Now adapts gracefully to narrow terminals. 🔍 Reverse Search: Press Ctrl+R in shell mode to instantly search command history. 🗜️ Customizable Context Compression: Set your threshold in settings.json. 📂 Multi-directory Support: Auto-load multiple dirs at startup. 🔢 Hide Line Numbers: Set "showLineNumbers": false for cleaner copy-paste. 🔕 Plus: Disable update nudges with "disableUpdateNag": true 📌More details: Upgrade now and level up your coding experience 💻🔥

Qwen

127,499 views • 11 months ago

An MCP server that detects production-grade code quality issues in real-time! Even though AI is now generating code at light speed, the engineering bottleneck has just moved from writing to reviewing, and now devs spend 90% of their debugging time on AI-generated code. AI reviewers aren't that reliable either because they share the same fundamental blind spots as AI generators do: - They pattern match, not proof check. - They validate syntax, not system behavior. - They review code, not consequences. I have been using the SonarQube MCP Server (by Sonar) to solve this. It produces enterprise-grade code analysis and returns instant feedback on bugs, vulnerabilities, and code smells right where you're working (Claude Code, Cursor, etc.). Its capabilities have emerged from the 750B+ lines of code SonarQube processes daily, so it has seen every bug pattern that exists. This includes: - Security vulnerabilities (SQL injection, XSS, hardcoded secrets) - Code smells and technical debt - Test coverage gaps - Maintainability issues The setup is simple: - Install the SonarQube MCP server - Add it to your AI assistant's config Done! SonarQube is now part of your AI coding workflow. I have shared the GitHub repo in the replies! P.S. Thanks to Sonar for working with me today.

An MCP server that detects production-grade code quality issues in real-time! Even though AI is now generating code at light speed, the engineering bottleneck has just moved from writing to reviewing, and now devs spend 90% of their debugging time on AI-generated code. AI reviewers aren't that reliable either because they share the same fundamental blind spots as AI generators do: - They pattern match, not proof check. - They validate syntax, not system behavior. - They review code, not consequences. I have been using the SonarQube MCP Server (by Sonar) to solve this. It produces enterprise-grade code analysis and returns instant feedback on bugs, vulnerabilities, and code smells right where you're working (Claude Code, Cursor, etc.). Its capabilities have emerged from the 750B+ lines of code SonarQube processes daily, so it has seen every bug pattern that exists. This includes: - Security vulnerabilities (SQL injection, XSS, hardcoded secrets) - Code smells and technical debt - Test coverage gaps - Maintainability issues The setup is simple: - Install the SonarQube MCP server - Add it to your AI assistant's config Done! SonarQube is now part of your AI coding workflow. I have shared the GitHub repo in the replies! P.S. Thanks to Sonar for working with me today.

Avi Chawla

28,027 views • 7 months ago

We’re expanding the Gemini API File Search tool 🔍 with 3 new updates that enable developers to more easily build multimodal RAG systems with enhanced precision: + Multimodal Support: By leveraging our Gemini Embedding 2 model, File Search can now reason across image and text simultaneously. + Custom Metadata Filtering: Bring structure to unstructured data by tagging files with custom key-value labels. This pre-filters your data and boosts search speed. + Exact citations: File Search can now capture and return the exact source (down to the page number) for every piece of information indexed. See multimodal File Search in action with our example app in Google AI Studio. Chat with your entire image and doc library, ask questions, and trace answers back to the source:

We’re expanding the Gemini API File Search tool 🔍 with 3 new updates that enable developers to more easily build multimodal RAG systems with enhanced precision: + Multimodal Support: By leveraging our Gemini Embedding 2 model, File Search can now reason across image and text simultaneously. + Custom Metadata Filtering: Bring structure to unstructured data by tagging files with custom key-value labels. This pre-filters your data and boosts search speed. + Exact citations: File Search can now capture and return the exact source (down to the page number) for every piece of information indexed. See multimodal File Search in action with our example app in Google AI Studio. Chat with your entire image and doc library, ask questions, and trace answers back to the source:

Google AI Developers

108,622 views • 2 months ago

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:

DeepLearning.AI

32,513 views • 11 months 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

🚀 Exciting Update Alert! 🚀 We’re thrilled to announce a major update coming soon for Reactor, our AI model! Here’s what’s new: 🔍 Enhanced Search Capabilities: - News Search: Stay up-to-date with the latest happenings. - X & YouTube Search: Find trending content and videos instantly. - Reddit Search: Dive into discussions and communities with ease. - Wolfram Alpha Integration: Solve complex math problems and get accurate answers. 📚 References & Research: - Access reliable references and data for your queries. 🖼️ Visual Search & Creation: - Image & Video Search: Find visuals faster than ever. - Image Generation: Create stunning images with AI-powered tools. And that’s not all! We’ve also improved performance and added behind-the-scenes enhancements to make Reactor even more intuitive and responsive. Try out the new features today and let us know what you think! Your feedback helps us keep improving. 💡

🚀 Exciting Update Alert! 🚀 We’re thrilled to announce a major update coming soon for Reactor, our AI model! Here’s what’s new: 🔍 Enhanced Search Capabilities: - News Search: Stay up-to-date with the latest happenings. - X & YouTube Search: Find trending content and videos instantly. - Reddit Search: Dive into discussions and communities with ease. - Wolfram Alpha Integration: Solve complex math problems and get accurate answers. 📚 References & Research: - Access reliable references and data for your queries. 🖼️ Visual Search & Creation: - Image & Video Search: Find visuals faster than ever. - Image Generation: Create stunning images with AI-powered tools. And that’s not all! We’ve also improved performance and added behind-the-scenes enhancements to make Reactor even more intuitive and responsive. Try out the new features today and let us know what you think! Your feedback helps us keep improving. 💡

ARC

87,518 views • 1 year ago

MCP is an absolute game-changer. (Together with DeepSeek, MCP is probably the hottest thing in AI over the last 6 months.) I use Cursor to write code 90% of the time. I built an MCP server to connect the Cursor agent to GroundX, an open-source RAG system, and I'm not going back. This is officially insane! Here is what I did, step by step: First, a little bit of context. I maintain an end-to-end Machine Learning System with several pipelines to process data, train, evaluate, register, deploy, and monitor a model. I've written a lot of documentation explaining how the system works and how to modify and maintain it. There's also the documentation of the few libraries I used to build the system. I'm a massive fan of GroundX, an open-source enterprise-grade RAG system you can run on your servers or deploy to any cloud provider. I've been working with them for a long time. GroundX offers two services. First, the "ingest" service uses a custom, pretrained vision model to ingest and understand your data. I used this to process all the documentation I have for my code. Markdown files, source code, HTML files, and even PDF documents. Everything I've written related to my project went into GroundX. Their second service is "search," which combines text and vector search with a fine-tuned re-ranker model to retrieve information from the data. I needed to connect Cursor with this service, and that's where MCP came in. I built an MCP server with two tools: 1. The first tool would go to GroundX and retrieve the available topics. Splitting the data into topics (or "buckets," as GroundX calls them) allows me to use the same setup to serve documentation from different topics. 2. The second tool would search GroundX under a specific topic for the context related to the supplied query. The magic happens after connecting the MCP server with Cursor. Now, I can ask any questions related to my project, and Cursor's AI agent retrieves the list of available topics from the RAG system and then searches it to provide relevant context to the model. I went from getting mediocre, sometimes wrong answers to 100% truthful, complete answers. Here is the crazy part:

MCP is an absolute game-changer. (Together with DeepSeek, MCP is probably the hottest thing in AI over the last 6 months.) I use Cursor to write code 90% of the time. I built an MCP server to connect the Cursor agent to GroundX, an open-source RAG system, and I'm not going back. This is officially insane! Here is what I did, step by step: First, a little bit of context. I maintain an end-to-end Machine Learning System with several pipelines to process data, train, evaluate, register, deploy, and monitor a model. I've written a lot of documentation explaining how the system works and how to modify and maintain it. There's also the documentation of the few libraries I used to build the system. I'm a massive fan of GroundX, an open-source enterprise-grade RAG system you can run on your servers or deploy to any cloud provider. I've been working with them for a long time. GroundX offers two services. First, the "ingest" service uses a custom, pretrained vision model to ingest and understand your data. I used this to process all the documentation I have for my code. Markdown files, source code, HTML files, and even PDF documents. Everything I've written related to my project went into GroundX. Their second service is "search," which combines text and vector search with a fine-tuned re-ranker model to retrieve information from the data. I needed to connect Cursor with this service, and that's where MCP came in. I built an MCP server with two tools: 1. The first tool would go to GroundX and retrieve the available topics. Splitting the data into topics (or "buckets," as GroundX calls them) allows me to use the same setup to serve documentation from different topics. 2. The second tool would search GroundX under a specific topic for the context related to the supplied query. The magic happens after connecting the MCP server with Cursor. Now, I can ask any questions related to my project, and Cursor's AI agent retrieves the list of available topics from the RAG system and then searches it to provide relevant context to the model. I went from getting mediocre, sometimes wrong answers to 100% truthful, complete answers. Here is the crazy part:

Santiago

255,433 views • 1 year ago

XBOW raised $117M to build AI hacking agents. Now someone just open-sourced it for FREE. Strix deploys autonomous AI agents that act like real hackers - they run your code dynamically, find vulnerabilities, and validate them through actual proof-of-concepts. Why it matters: The biggest problem with traditional security testing is that it doesn't keep up with development speed. Strix solves this by integrating directly into your workflow: ↳ Run it in CI/CD to catch vulnerabilities before production ↳ Get real proof-of-concepts, not false positives from static analysis ↳ Test everything: injection attacks, access control, business logic flaws The best part? You don't need to be a security expert. Strix includes a complete hacker toolkit - HTTP proxy, browser automation, and Python runtime for exploit development. It's like having a security team that works at the speed of your CI/CD pipeline. The best part is that the tool runs locally in Docker containers, so your code never leaves your environment. Getting started is simple: - pipx install strix-agent - Point it at your codebase (app, repo, or directory) Everything is 100% open-source! I've shared link to the GitHub repo in the replies!

XBOW raised $117M to build AI hacking agents. Now someone just open-sourced it for FREE. Strix deploys autonomous AI agents that act like real hackers - they run your code dynamically, find vulnerabilities, and validate them through actual proof-of-concepts. Why it matters: The biggest problem with traditional security testing is that it doesn't keep up with development speed. Strix solves this by integrating directly into your workflow: ↳ Run it in CI/CD to catch vulnerabilities before production ↳ Get real proof-of-concepts, not false positives from static analysis ↳ Test everything: injection attacks, access control, business logic flaws The best part? You don't need to be a security expert. Strix includes a complete hacker toolkit - HTTP proxy, browser automation, and Python runtime for exploit development. It's like having a security team that works at the speed of your CI/CD pipeline. The best part is that the tool runs locally in Docker containers, so your code never leaves your environment. Getting started is simple: - pipx install strix-agent - Point it at your codebase (app, repo, or directory) Everything is 100% open-source! I've shared link to the GitHub repo in the replies!

Akshay 🚀

96,623 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

We’re excited to release an interactive guide highlighting the definitive set of principles for building AI agents 🔥 Based on the popular 12-Factor agents repo by dex. We packaged the principles into an interactive website and Colab notebook with working code examples, so that you can incorporate these principles into your agent application in minutes. As a refresher - some of the principles include getting structured outputs from tools, state management, checkpointing, human-in-the-loop, error handling, composing smaller agents into bigger ones, and more. This is amazing work by Laurie Voss and should be required reading for anyone looking to get started building agents. Website: Notebook:

We’re excited to release an interactive guide highlighting the definitive set of principles for building AI agents 🔥 Based on the popular 12-Factor agents repo by dex. We packaged the principles into an interactive website and Colab notebook with working code examples, so that you can incorporate these principles into your agent application in minutes. As a refresher - some of the principles include getting structured outputs from tools, state management, checkpointing, human-in-the-loop, error handling, composing smaller agents into bigger ones, and more. This is amazing work by Laurie Voss and should be required reading for anyone looking to get started building agents. Website: Notebook:

Jerry Liu

33,740 views • 1 year ago

After testing every AI toolchain for a year, this is the only workflow I'm endorsing: Figma → Claude Code (Sonnet 4.5) Here's how I'm building Lorelight: 1. Design the happy path in Figma 2. Feed it to Claude Code via Figma MCP 3. Get a strong foundation in 15 minutes 4. Spend the next 50+ hours iterating (still design work) The design/dev loop that used to be isolated is finally closing. I even built a complete design system in within minutes. Custom components, living documentation, accessibility rules all from the Figma designs. If you're measuring the effectiveness of these tools by whether they can "one-shot" a design, you're consuming too many AI hype commercials. Log off. Then go actually build something with your hands. With AI. With your taste. Stop trying to "one-shot" good shit. And go make stuff with the new tools. For designers who want to ship real products, this is the stack that's getting it done.

After testing every AI toolchain for a year, this is the only workflow I'm endorsing: Figma → Claude Code (Sonnet 4.5) Here's how I'm building Lorelight: 1. Design the happy path in Figma 2. Feed it to Claude Code via Figma MCP 3. Get a strong foundation in 15 minutes 4. Spend the next 50+ hours iterating (still design work) The design/dev loop that used to be isolated is finally closing. I even built a complete design system in within minutes. Custom components, living documentation, accessibility rules all from the Figma designs. If you're measuring the effectiveness of these tools by whether they can "one-shot" a design, you're consuming too many AI hype commercials. Log off. Then go actually build something with your hands. With AI. With your taste. Stop trying to "one-shot" good shit. And go make stuff with the new tools. For designers who want to ship real products, this is the stack that's getting it done.

Tommy Geoco

22,905 views • 9 months ago

Today is a big one: Slack AI is shipping to GA for our enterprise customers 🚀 It’s a native, trusted way to get the benefits of generative AI in the flow of working in Slack: 🔍 Get concise answers and suggested Q&A with AI-powered search ⏪ Catch up quickly with channel recaps 🧵 Get the gist of long conversations with thread summaries I’ve been blown away by this team, which didn’t even exist a year ago. Now they’re shipping new capabilities that unlock the collective knowledge stored in channels. It gives people back time spent searching and catching up, so they have more time to do the creative parts of their jobs. This is only the beginning and we can’t wait to see what everyone does with it! Learn more and sign-up at

Today is a big one: Slack AI is shipping to GA for our enterprise customers 🚀 It’s a native, trusted way to get the benefits of generative AI in the flow of working in Slack: 🔍 Get concise answers and suggested Q&A with AI-powered search ⏪ Catch up quickly with channel recaps 🧵 Get the gist of long conversations with thread summaries I’ve been blown away by this team, which didn’t even exist a year ago. Now they’re shipping new capabilities that unlock the collective knowledge stored in channels. It gives people back time spent searching and catching up, so they have more time to do the creative parts of their jobs. This is only the beginning and we can’t wait to see what everyone does with it! Learn more and sign-up at

Noah Desai Weiss

13,312 views • 2 years ago