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Agent-in-the-browser unlocks superpowers. The magic really shines when you bring your own model, system prompts, and MCP tools into the mix. Hence, AgentBoard 🪄. Your models, keys, and settings. Integration with remote MCP servers and — this is where it gets really fun — ability to register and allow...

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Ilya Grigorik

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22,961 次观看 • 8 个月前 •via X (Twitter)

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wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.
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wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.

Molly O’Shea

20,408 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

81,563 次观看 • 9 个月前

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!

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142,010 次观看 • 1 年前

Your standards. Your tools. Every review. Copilot code review supports custom agent skills and MCP server connections. Now in public preview for existing Copilot Pro, Pro+, Business, and Enterprise users. Bring your org's context and standards right into the diff. 💡
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Your standards. Your tools. Every review. Copilot code review supports custom agent skills and MCP server connections. Now in public preview for existing Copilot Pro, Pro+, Business, and Enterprise users. Bring your org's context and standards right into the diff. 💡

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🔧 Bring Your Own Model — and Let Agent Forge Handle the Rest! John plugged in his own custom model — and in minutes, it was up and running as a fully deployed AI Agent. With Agent Forge, you’re not limited to prebuilt tools. You bring the intelligence. We automate everything else. Real models. Real-time deployment. Zero-code friction. 🔗 Try it here:

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

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376,455 次观看 • 3 个月前

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

Replit ⠕

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Introducing SperaxOS Plugin Store Browse & install plugins right from the chat. Choose from many of your favorite apps, plugins, and tools or bring your own custom MCP Servers. Watch how anyone can import any MCP Server into SperaxOS chat in under 1 minute.
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Introducing SperaxOS Plugin Store Browse & install plugins right from the chat. Choose from many of your favorite apps, plugins, and tools or bring your own custom MCP Servers. Watch how anyone can import any MCP Server into SperaxOS chat in under 1 minute.

Sperax | SperaxOS

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using tools, Cursor Agent have the capability to reason about when it should - read & write code - search your codebase - call MCP servers - run terminal commands an example where it connects to a postgres database, runs a query and writes to file
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using tools, Cursor Agent have the capability to reason about when it should - read & write code - search your codebase - call MCP servers - run terminal commands an example where it connects to a postgres database, runs a query and writes to file

eric zakariasson

34,917 次观看 • 1 年前

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HERMES AGENT WITHOUT TOOLS IS A CHATBOT. WITH THEM IT BUILDS 3D TOWERS IN BLENDER, CHECKS STOCK PRICES, AND DRIVES VS CODE. tonbi JUST DROPPED THE FULL GUIDE. module 6 of his 10-part Hermes masterclass. best breakdown of the tool layer anyone has published. what you need to know: TOOLS vs SKILLS vs MCP skills = instructions (markdown, loaded into context) tools = callable functions (Python, agent emits call, Hermes executes) MCP = adapters to external systems (Blender, Stripe, Linear, Notion) every tool has three parts: → the function (does the real work) → the schema (what the model sees to decide when to call it) → the registry (makes the tool exist in the agent) the model never runs the function directly. it emits a structured request (tool name + JSON args). Hermes executes and returns the result. if the tool fails, the error goes back as JSON. the agent recovers instead of crashing. TOOL SETS CONTROL THE SURFACE hermes chat --tool-sets web # only web tools loaded. no files, no terminal. hermes chat --tool-sets safe # read-only: web search, vision, image gen. # no file writes. no terminal. no code execution. mid-session: /tools enable video /tools disable terminal or toggle in the dashboard: hermes dashboard → Skills → Tool Sets MCP SERVERS two transport types: STDIO (local subprocess) or HTTP (remote endpoint). add a server: hermes mcp # or ask: "add the MCP server for Blender" filter tools with include/exclude per server. keep only what you trust. security: → OAuth 2.1 PKCE (no long-lived tokens in config) → package scanning via api.osv. dev before launch → all MCP calls go through approval gates HERMES AS MCP SERVER hermes mcp serve exposes 10 tools via FastMCP. connect VS Code Copilot or Cursor to your running Hermes instance. BUILD YOUR OWN TOOL He built a stock price tool live: → Python function calling Finnhub API → schema with name, description, parameters → registered in tool_sets.py → agent calls it automatically when relevant any repeating API call in your workflow can become a native tool. full Hermes architecture deep-dive in the article 👇

YanXbt

32,680 次观看 • 17 天前

🔧 Let your coding agent identify and fix INP issues for you → With Chrome DevTools MCP, your coding agent can measure performance for specific user interactions to find and resolve slow UI responses - on its own with the help of the browser!
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🔧 Let your coding agent identify and fix INP issues for you → With Chrome DevTools MCP, your coding agent can measure performance for specific user interactions to find and resolve slow UI responses - on its own with the help of the browser!

Chrome for Developers

16,696 次观看 • 8 个月前

Claude Code can now connect to remote MCP servers. Pull context from your tools directly into Claude Code with no local setup required.
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Claude Code can now connect to remote MCP servers. Pull context from your tools directly into Claude Code with no local setup required.

Anthropic

540,378 次观看 • 1 年前

Subagents have arrived in Gemini CLI! 🤖🚀 Create your own custom subagents in Gemini CLI! Subagents are specialized, expert agents that the main agent can delegate work to. 📦- Subagents have their own set of tools, MCP servers, system instructions, and context window. 🏷️- Use Agent to explicitly delegate to a subagent 🧹- Keeps the main context window clean ⚡️- Speed up work by running agents in parallel Read more in the launch blog below 👇
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Subagents have arrived in Gemini CLI! 🤖🚀 Create your own custom subagents in Gemini CLI! Subagents are specialized, expert agents that the main agent can delegate work to. 📦- Subagents have their own set of tools, MCP servers, system instructions, and context window. 🏷️- Use Agent to explicitly delegate to a subagent 🧹- Keeps the main context window clean ⚡️- Speed up work by running agents in parallel Read more in the launch blog below 👇

Jack Wotherspoon

221,592 次观看 • 2 个月前

PSA: Groq Inc now supports remote MCP server integration (BETA). ⚡️ LLMs are only as good as the context you give them and MCP enables you to connect them directly to live APIs, databases, and tools. Now your models can pull fresh data, perform actions, AND respond in seconds.
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PSA: Groq Inc now supports remote MCP server integration (BETA). ⚡️ LLMs are only as good as the context you give them and MCP enables you to connect them directly to live APIs, databases, and tools. Now your models can pull fresh data, perform actions, AND respond in seconds.

Hatice Ozen

13,829 次观看 • 9 个月前

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🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.

OpenServ

34,665 次观看 • 1 年前

Introducing Vapi's MCP client! Your agent can now call tools from any MCP server (like Zapier or Composio), live in conversations. Access hundreds of tools without any custom integrations. Examples and docs👇
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Introducing Vapi's MCP client! Your agent can now call tools from any MCP server (like Zapier or Composio), live in conversations. Access hundreds of tools without any custom integrations. Examples and docs👇

Vapi

43,181 次观看 • 1 年前

How to add Chrome DevTools MCP to Claude Code (Bookmark this post to follow the setup later) This lets Claude debug your web apps in a real browser... performance traces, network inspection, console errors. Requirements: - Node.js 22+ - Chrome browser In the video below I show you: - How to install the MCP server - How to verify the DevTools tools are available - How to use it with a real performance trace
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How to add Chrome DevTools MCP to Claude Code (Bookmark this post to follow the setup later) This lets Claude debug your web apps in a real browser... performance traces, network inspection, console errors. Requirements: - Node.js 22+ - Chrome browser In the video below I show you: - How to install the MCP server - How to verify the DevTools tools are available - How to use it with a real performance trace

Daniel San

52,294 次观看 • 8 个月前

Introducing Tidewave Web for Rails and Phoenix: a coding agent that runs in the browser alongside your web application, with full page and code context. Tidewave deeply integrates with your stack, from the database to the UI, making AI development more seamless and efficient.
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Introducing Tidewave Web for Rails and Phoenix: a coding agent that runs in the browser alongside your web application, with full page and code context. Tidewave deeply integrates with your stack, from the database to the UI, making AI development more seamless and efficient.

José Valim

97,922 次观看 • 10 个月前