We\u2019ve all experienced the use of AI in our daily coding tasks. Many of us may have used Repl.it, an online code editor with built-in AI capabilities that help generate code and offer guidance. Likewise, tools like GitHub Copilot<\/a> for VS Code<\/a> enhance our coding workflow through simple extensions. And if you\u2019ve ever tried Black Box AI<\/a> or Cursor IDE<\/a>, you know how inbuilt AI assistance can simplify your work \ud83d\ude09<\/p>\n Imagine if these AI tools could tap into any data source\u2014whether it\u2019s your local files or remote services\u2014without needing custom code for every connection. That\u2019s the promise of the Model Context Protocol (MCP)<\/strong>.<\/p>\n MCP is an open standard that creates secure, two-way connections between your data and AI-powered tools. Think of it like a USB-C port for AI applications\u2014a single, common connector that lets different tools and data sources \u201ctalk\u201d to each other.<\/p>\n As AI assistants become part of our daily workflow (think Replit<\/a> with its built-in AI or GitHub Copilot<\/a> for VS Code<\/a>), the challenge is making sure they have all the context they need. Today, every new data source often requires custom code, which is both messy and time-consuming.<\/p>\n MCP simplifies things by:<\/strong><\/p>\n \u201cAt Block, open source is more than a development model\u2014it\u2019s the foundation of our work and a commitment to creating technology that drives meaningful change. Open technologies like the Model Context Protocol connect AI to real-world applications in an accessible, transparent, and collaborative way.\u201d Early adopters\u2014companies like Block and Apollo\u2014are already integrating MCP into their systems. Additionally, development tool companies such as Zed<\/a>, Replit, Codeium<\/a>, and Sourcegraph<\/a> are exploring MCP. This means AI agents can retrieve more relevant information to better understand coding tasks, resulting in higher-quality code with fewer iterations.<\/p>\n MCP follows a client-server architecture<\/strong>. Here\u2019s what that means:<\/p>\n After initialization, the following patterns are supported:<\/p>\n Either party can terminate the connection:<\/p>\n Here’s a quick demo using the Claude desktop app, where we’ve configured MCP:<\/p>\n Watch Claude connect directly to GitHub, create a new repo, and make a PR through a simple MCP integration.<\/p>\n Once MCP was set up in Claude desktop, building this integration took less than an hour. pic.twitter.com\/xseX89Z2PD<\/a><\/p>\n \u2014 Alex Albert (@alexalbert__) November 25, 2024<\/a><\/p><\/blockquote>\n Here’s a quick demo using the Claude desktop app, where we’ve configured MCP:<\/p>\n Watch Claude connect directly to GitHub, create a new repo, and make a PR through a simple MCP integration.<\/p>\n Once MCP was set up in Claude desktop, building this integration took less than an hour. pic.twitter.com\/xseX89Z2PD<\/a><\/p>\n \u2014 Alex Albert (@alexalbert__) November 25, 2024<\/a><\/p><\/blockquote>\n<\/div>\n<\/figure>\n Alex Albert (@alexalbert__)<\/strong> explains it well on X<\/a>:<\/p>\n MCP is designed to be quick to set up. You can have a working integration in under 5 minutes<\/strong> with pre-built servers for platforms like GitHub, Slack, SQL databases, local files, and search engines.<\/p>\n Tools<\/strong> in MCP allow servers to expose executable functions\u2014imagine them as specialized \u201caction buttons\u201d that AI models can press to perform tasks, run calculations, or interact with external systems. With tools, an AI can not only understand your data but also act upon it, making it a powerful bridge between ideas and execution.<\/p>\n Each tool has a unique name, an optional human-friendly description, and a JSON schema that specifies what parameters it expects. For example:<\/p>\n Stay up-to-date with the most recent developments in the Model Context Protocol (MCP):<\/p>\n We\u2019re excited to announce that the Java SDK, developed by Spring AI at VMware Tanzu, is now the official Java SDK for MCP. This new addition joins our existing Kotlin SDK, and the Spring AI team will maintain it as a key part of the MCP community.<\/p>\n Version 1.2.1 of the MCP Python SDK is here, delivering important stability improvements and bug fixes.<\/p>\nWhat is MCP?<\/h2>\n
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Why MCP Matter?<\/h2>\n
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\u2014 Dhanji R. Prasanna, CTO at<\/em> Block<\/em><\/a><\/p>\nReal-World Impact and Early Adoption<\/h2>\n
How MCP Works: A Simple Breakdown<\/h2>\n
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1. Initialization<\/h3>\n
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2. Message exchange<\/strong><\/h3>\n
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3. Termination<\/strong><\/h3>\n
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Watch it in Action<\/h2>\n
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What Experts Are Saying<\/strong>?<\/h2>\n
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<\/strong>\u201cGetting LLMs to interact with external systems isn\u2019t usually that easy. Today, every developer needs to write custom code to connect their LLM apps with data sources. It\u2019s messy, repetitive work.\u201d<\/li>\n
<\/strong>\u201cMCP fixes this with a standard protocol for sharing resources, tools, and prompts.\u201d<\/li>\n\n
Get Started with MCP<\/h2>\n
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<\/strong>Install the Claude Desktop app<\/a> and follow the instructions on the Model Context Protocol website<\/a> to connect your first server.<\/li>\n
<\/strong>Learn how to build your own MCP server and contribute to the protocol on GitHub<\/a>.<\/li>\n
<\/strong>For an in-depth look, check out this blog post by Anthropic<\/a> and even have Claude explain MCP to you by feeding it the complete documentation available here<\/a>.<\/li>\n<\/ul>\nWhat Are Tools?<\/h3>\n
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How Are Tools Defined?<\/h3>\n
Latest Outcomes<\/h2>\n
2025-02-14: Java SDK Released<\/h3>\n
2025-01-27: Python SDK 1.2.1<\/h4>\n
2025-01-18: SDK and Server Improvements<\/h4>\n
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2025-01-03: More SDK and Server Enhancements<\/h4>\n
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