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Leverage the Dart MCP Server to enhance your Dart and Flutter development experience 💻✨ In this blog, explore useful tools provided by the Dart MCP Server and learn how to configure the server for use in popular development environments and tools →

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18,914 次观看 • 11 个月前 •via X (Twitter)

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Dart & Flutter MCP Server, Dart CLI improvements, and Widget Previews are all packed into Flutter 3.35 💫 Get the details ↓
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Dart & Flutter MCP Server, Dart CLI improvements, and Widget Previews are all packed into Flutter 3.35 💫 Get the details ↓

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14,594 次观看 • 9 个月前

Did you know that you can edit your code and add packages to your Flutter app by connecting the Flutter Extension for Gemini CLI with the Dart & Flutter MCP server? Github →
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Did you know that you can edit your code and add packages to your Flutter app by connecting the Flutter Extension for Gemini CLI with the Dart & Flutter MCP server? Github →

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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!

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Explore the dart_mcp package, a powerful framework for building Model Context Protocol (MCP) servers and clients in Dart, enabling you to connect AI models to resources, tools, and create custom workflows.
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Explore the dart_mcp package, a powerful framework for building Model Context Protocol (MCP) servers and clients in Dart, enabling you to connect AI models to resources, tools, and create custom workflows.

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23,120 次观看 • 2 个月前

Automating Flutter feature testing and App Store screenshots is easy! Developers can control running Flutter apps and capture real-time screenshots directly through the AGY browser. As long as you install the Dart MCP server and flutter_driver, you're ready to go.
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Automating Flutter feature testing and App Store screenshots is easy! Developers can control running Flutter apps and capture real-time screenshots directly through the AGY browser. As long as you install the Dart MCP server and flutter_driver, you're ready to go.

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Search Stripe knowledge and call Stripe APIs directly in Cursor using the new Stripe MCP server! Use this command to get started: npx -y Stripe/mcp --tools=all --api-key=STRIPE_SECRET_KEY
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How to set up Sentry MCP server with Warp 👉Open MCP Server > Add Server 👉Paste in the Sentry MCP JSON 👉Press "Start" and go through authentication process 👉Copy the Sentry link you want to fix 👉Ask Agent Mode to fix the issue

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