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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...

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Dan Shipper 📧

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15,645 次观看 • 9 个月前 •via X (Twitter)

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New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!
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New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,010 次观看 • 1 年前

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

Andrew Ng

81,696 次观看 • 10 个月前

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.
6:37

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 次观看 • 1 年前

Today, we're supporting MCP in Flowise! There has been a lot of discussion about MCP lately. From our perspective, MCP is nothing fancy - just tools with third-party APIs or functions, wrapped under a unified protocol that can be consumed by client LLM applications. In fact, we've had something similar for a long time with Langchain Tools. However, MCP offers two key benefits: - Its open protocol isn’t limited to specific frameworks - Client applications (like us, Cursor , @windsurf , etc) can offload the burden of maintaining and updating these tools to the official MCP creators These MCP servers (a.k.a. tools) are provided to the LLM, allowing it to decide when and how to use them. This is sometimes referred to as an "agentic" approach, as opposed to a "deterministic" approach like Zapier, where each step must be explicitly mapped out. In real-world scenarios, you might find yourself needing both approaches depending on the use case. It's still early days, and it will take time for the community to establish standards. We're excited to see where this goes!
0:46

Today, we're supporting MCP in Flowise! There has been a lot of discussion about MCP lately. From our perspective, MCP is nothing fancy - just tools with third-party APIs or functions, wrapped under a unified protocol that can be consumed by client LLM applications. In fact, we've had something similar for a long time with Langchain Tools. However, MCP offers two key benefits: - Its open protocol isn’t limited to specific frameworks - Client applications (like us, Cursor , @windsurf , etc) can offload the burden of maintaining and updating these tools to the official MCP creators These MCP servers (a.k.a. tools) are provided to the LLM, allowing it to decide when and how to use them. This is sometimes referred to as an "agentic" approach, as opposed to a "deterministic" approach like Zapier, where each step must be explicitly mapped out. In real-world scenarios, you might find yourself needing both approaches depending on the use case. It's still early days, and it will take time for the community to establish standards. We're excited to see where this goes!

FlowiseAI

27,360 次观看 • 1 年前

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:
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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 次观看 • 1 年前

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.
14:14

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

Santiago

142,696 次观看 • 1 年前

🚀 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. 🔮
0:30

🚀 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 次观看 • 1 年前

Claude Code is terrible at UI design and everyone knows it so this guy fixed it by building an MCP that gives Claude its own AI design tool instead of going back and forth between a design platform and your code editor, Claude now creates the designs itself and drops them straight into your codebase the MCP has full context of your existing design system and project so everything it generates actually matches what you already have. one command to set up and it installs the MCP and skill files so Claude instantly knows how to use it if you're tired of the same Inter font, purple gradient, card grid layout on every project, this is definitely worth trying
0:58

Claude Code is terrible at UI design and everyone knows it so this guy fixed it by building an MCP that gives Claude its own AI design tool instead of going back and forth between a design platform and your code editor, Claude now creates the designs itself and drops them straight into your codebase the MCP has full context of your existing design system and project so everything it generates actually matches what you already have. one command to set up and it installs the MCP and skill files so Claude instantly knows how to use it if you're tired of the same Inter font, purple gradient, card grid layout on every project, this is definitely worth trying

Om Patel

398,097 次观看 • 3 个月前

Sharing the 4 Claude MCP servers I use the most lately. 1. TinyFish MCP lets Claude browse the live web, scrape, research, and return structured data instead of static answers. I’ve been using it to gather AI news for my newsletter, like pulling top Hacker News posts from the last few hours and summarizing them into a clean list. 2. GitHub MCP helps me review commits, trace changes, and understand repos without jumping between tools. 3. Figma MCP lets me inspect designs, check layout and spacing, and pull UI details directly into the conversation. 4. Excalidraw MCP is great for thinking visually. I use it to sketch flows and system ideas when text isn’t enough. The last few are probably familiar, but the first one is worth a look. I recorded a short video showing how it works in practice.
0:28

Sharing the 4 Claude MCP servers I use the most lately. 1. TinyFish MCP lets Claude browse the live web, scrape, research, and return structured data instead of static answers. I’ve been using it to gather AI news for my newsletter, like pulling top Hacker News posts from the last few hours and summarizing them into a clean list. 2. GitHub MCP helps me review commits, trace changes, and understand repos without jumping between tools. 3. Figma MCP lets me inspect designs, check layout and spacing, and pull UI details directly into the conversation. 4. Excalidraw MCP is great for thinking visually. I use it to sketch flows and system ideas when text isn’t enough. The last few are probably familiar, but the first one is worth a look. I recorded a short video showing how it works in practice.

Tw93

37,716 次观看 • 3 个月前

This Composio connect-apps plugin for Claude Code is 🔥 It's the easiest way to instantly connect Claude Code to 500+ apps like Gmail, Slack, GitHub, and Linear. You really don't need to be setting up MCP servers one by one. I use it a lot, and it has saved me a ton of time.
1:02

This Composio connect-apps plugin for Claude Code is 🔥 It's the easiest way to instantly connect Claude Code to 500+ apps like Gmail, Slack, GitHub, and Linear. You really don't need to be setting up MCP servers one by one. I use it a lot, and it has saved me a ton of time.

elvis

33,729 次观看 • 5 个月前

CLAUDE IS SO MUCH BETTER WHEN YOU USE MCP Both Claude Sonnet and Opus are Insanely smart Agents. But they can't read your mind. You need to Give it Context... (MCP = model CONTEXT protocol) Here is an example... Then a tutorial on how to give Claude access to MCP tools without writing a single line of code or copying any "JSON". TIME STAMPS: 00:00 - Introduction 01:00 - First Example with Notion - Searching, Research, Scripting 02:53 - Generating 5 YouTube Thumbnails GLIF - Adding Auto Comments to Notion 04:52 - Claude Project System Prompt - (Will make follow up video) 05:13 - How to set up these MCP's
7:53

CLAUDE IS SO MUCH BETTER WHEN YOU USE MCP Both Claude Sonnet and Opus are Insanely smart Agents. But they can't read your mind. You need to Give it Context... (MCP = model CONTEXT protocol) Here is an example... Then a tutorial on how to give Claude access to MCP tools without writing a single line of code or copying any "JSON". TIME STAMPS: 00:00 - Introduction 01:00 - First Example with Notion - Searching, Research, Scripting 02:53 - Generating 5 YouTube Thumbnails GLIF - Adding Auto Comments to Notion 04:52 - Claude Project System Prompt - (Will make follow up video) 05:13 - How to set up these MCP's

Riley Brown

344,940 次观看 • 1 年前

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇
2:24

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

Taranjeet

471,767 次观看 • 1 年前

MCP is a pain in the ass. To fix that, we’re launching DAuth: the first secure, multi-tenant MCP auth solution. OAuth isn’t enough. It doesn't support dynamically passing API keys, and you can’t trust passing credentials to non-OAuth servers, since secrets are exposed. This forces existing MCP marketplaces to be OAuth-centric and single-tenant. But OAuth is annoying to implement and assumes security expertise. DAuth works for every auth case. We encrypt your credentials on the client-side, guaranteeing MCP servers never see them. You can finally ship agents with peace of mind. For the first time, MCP is ready for takeoff 🛫
1:54

MCP is a pain in the ass. To fix that, we’re launching DAuth: the first secure, multi-tenant MCP auth solution. OAuth isn’t enough. It doesn't support dynamically passing API keys, and you can’t trust passing credentials to non-OAuth servers, since secrets are exposed. This forces existing MCP marketplaces to be OAuth-centric and single-tenant. But OAuth is annoying to implement and assumes security expertise. DAuth works for every auth case. We encrypt your credentials on the client-side, guaranteeing MCP servers never see them. You can finally ship agents with peace of mind. For the first time, MCP is ready for takeoff 🛫

Dedalus Labs

1,037,030 次观看 • 5 个月前

A designer who can ship code is one of the most valuable hires in tech today. Here's my new episode with Felix Lee (CEO of ADPList) where he showed me how to use Claude Code and Figma MCP to go from: ✅ Figma → Code: Design to website in 15 min ✅ Code → Figma: App back to editable design Felix also demoed a few apps he's designed and built in Claude Code, including a 3D globe website and a landing page analyzer. Some takeaways from Felix: 1. Stop pasting screenshots into Claude Code. Instead, use Figma MCP to pull in all your assets, icons, and images automatically. 2. You can use the same MCP to convert code back into editable Figma layers to explore more variations. 3. An underrated way to build: Use FigJam to plan a flowchart of your app first, then paste the link into Claude Code with the MCP. 📌 Watch now: Thanks to our sponsors: Replit ⠕: Plan, design, and build with AI agents Linear: The AI agent platform for modern teams
0:48

A designer who can ship code is one of the most valuable hires in tech today. Here's my new episode with Felix Lee (CEO of ADPList) where he showed me how to use Claude Code and Figma MCP to go from: ✅ Figma → Code: Design to website in 15 min ✅ Code → Figma: App back to editable design Felix also demoed a few apps he's designed and built in Claude Code, including a 3D globe website and a landing page analyzer. Some takeaways from Felix: 1. Stop pasting screenshots into Claude Code. Instead, use Figma MCP to pull in all your assets, icons, and images automatically. 2. You can use the same MCP to convert code back into editable Figma layers to explore more variations. 3. An underrated way to build: Use FigJam to plan a flowchart of your app first, then paste the link into Claude Code with the MCP. 📌 Watch now: Thanks to our sponsors: Replit ⠕: Plan, design, and build with AI agents Linear: The AI agent platform for modern teams

Peter Yang

77,115 次观看 • 3 个月前

Launching AI Discovery And Communication DeepAgent discovers other AI agents and software tools on the fly and communicates with them to perform complex tasks. Here is how it works - - DeepAgent searches for other AI agents and tools - Finds the right tool for the job - Establishes contact using MCP and handles auth - Collaborates with the other tool to get the job done! - Remembers this tool for future colabs! Much like humans, it can create a team of AI agents or software tools to perform tasks. Here it is, learning about MindMaps, communicating with another server, and creating a Mindmap!!
0:31

Launching AI Discovery And Communication DeepAgent discovers other AI agents and software tools on the fly and communicates with them to perform complex tasks. Here is how it works - - DeepAgent searches for other AI agents and tools - Finds the right tool for the job - Establishes contact using MCP and handles auth - Collaborates with the other tool to get the job done! - Remembers this tool for future colabs! Much like humans, it can create a team of AI agents or software tools to perform tasks. Here it is, learning about MindMaps, communicating with another server, and creating a Mindmap!!

Bindu Reddy

13,598 次观看 • 1 年前

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.
0:40

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 次观看 • 1 年前

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:
3:08

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,315 次观看 • 8 个月前

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.
0:33

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.

Blaze

55,926 次观看 • 2 个月前

Claude Code cracked something open for us Every 📧. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12
1:10:11

Claude Code cracked something open for us Every 📧. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12

Dan Shipper 📧

57,568 次观看 • 8 个月前