MCP (Model Context Protocol): The Standard That Wants to Be the USB of Artificial Intelligence

Anthropic's Model Context Protocol (MCP) aims to become a universal standard for connecting AI models with data sources and tools, analogous to USB for chargers. The protocol separates architecture into hosts and servers, enabling any MCP-compatible model to dynamically discover and use tools without custom integrations. A developer demonstrated MCP's portability by building a universal Python server for Supabase inventory that works with any MCP-compatible application.

If you've ever tried building an AI agent system in production, you know the pain. During the construction of the agentic radar for Obsolescence https://dev.to/en/posts/obs part5 radar agent/ , I faced the problem of connecting Gemini 2.5 with my Supabase database and an external API. I had to write custom code to adapt the tool schema Tool Calling to the exact format that Google demands. If tomorrow I decided to migrate that exact same system to Anthropic's Claude or OpenAI's GPT-4o, I would have to rewrite the entire tool integration layer because each vendor uses its own JSON dialect and its own argument validation logic Function Calling . It's the same chaos we lived through in the late 90s with mobile phone chargers: every brand had its proprietary connector. Then USB arrived and unified everything. That is exactly the ambition behind MCP Model Context Protocol : to become the USB of Artificial Intelligence. Initially proposed by Anthropic and rapidly adopted by a coalition of open-source companies, MCP is an open standard for connecting AI models with data sources and tools. The design premise is elegantly simple, separating the architecture into two independent pieces: The magic happens in the middle. The Host the LLM tells the MCP Server: "What tools and resources do you have available?" . The server responds in a universal format. From there, the LLM can read, write, or execute actions without the developer having had to write a proprietary integration between that specific model and that specific tool . On this blog, I have vehemently defended why Tool Calling is infinitely superior to traditional RAG for industrial applications that require precision. MCP does not replace Tool Calling; it standardizes it. Let's look at the architectural difference: | Feature | Classic Tool Calling | MCP Model Context Protocol | |---|---|---| Integration | 1-to-1 Specific Model ↔ Specific Tool | N-to-M Any Model ↔ Any MCP Server | Format | Dictated by the LLM vendor Google, OpenAI | Standard, agnostic JSON-RPC 2.0 | Discovery | Developer injects tools into the prompt | Host discovers tools dynamically | Portability | None. Migrating LLMs requires refactoring. | Total. You write the MCP server once. | To illustrate why this changes the game for operations and backend engineers, let's imagine we want to expose a Supabase table e.g., critical component inventory to our LLM. With a traditional CrewAI or Langchain approach, we would write a custom tool bound to that framework. With MCP, we write a universal Python server using the official SDK: python from mcp.server.fastmcp import FastMCP import supabase Initialize the MCP server mcp = FastMCP "Supabase Inventory Server" db = supabase.create client URL, KEY @mcp.tool def get critical stock part number: str - str: """Fetches the stock level of a specific component.""" response = db.table "inventory" .select "stock" .eq "pn", part number .execute if not response.data: return "Component not found." stock = response.data 0 'stock' return f"The current stock for {part number} is {stock} units." if name == " main ": The server starts and listens for requests over stdio JSON-RPC mcp.run That code block is all you need. Once running, any MCP-compatible application including Claude's official interface can connect to this server, read the function's description docstring , and decide when to call get critical stock with the correct arguments. The history of software is littered with "universal standards" that only managed to add one more standard to the list of competing standards. Will MCP survive? It has two massive advantages in its favor. The first is that it solves a real, acute pain point for corporate developers, who are sick of rewriting integrations every time a new model comes out. The second is the local-first approach . Standard MCP communication uses stdio standard input/output , which means the MCP server runs locally on your machine or private network. This is a wet dream for industrial cybersecurity because the data never leaves your infrastructure until the LLM explicitly and authorizedly requests it. However, MCP's success will depend on adoption by the dominant duopoly: Google and OpenAI. If Anthropic manages to create a large enough open-source ecosystem like Kubernetes once did against proprietary clouds , the other giants will be forced to support it natively. If you are designing the architecture for an Agentic Project Management Office https://dev.to/en/posts/proj ops part2 agentic pmo/ or any system where you need to connect AI agents with legacy ERPs, PLMs, or document repositories, my recommendation is to bet on isolating your connectors. Today it might be through independent Python functions, and tomorrow, probably, wrapping those same functions in an MCP Server. Just as USB killed hundreds of proprietary connectors, MCP has the potential to finally democratize LLM access to the "muscle" of enterprise data.