New & Emerging

Difference Between MCP and WebMCP

Last updated 2026-07-31

What's new

2026-07-31
  • **Model Context Protocol (MCP) (a way for different AI tools to talk to each other and share features) can help AI tools discover and use each other's features without needing custom code each time, but it doesn't replace the original services.**
  • **MCP can make maintaining and securing AI tools easier when the same feature is used across multiple tools, but it adds complexity and isn't free.**
  • **MCP is useful when a feature needs to be used by multiple AI tools, but for single-tool use, a direct API (a way for software to talk to each other) might be simpler.**
  • **Hostinger offers four ways to run AI agents on your own computers, including Open Claw (a specific AI tool), and you can get a discount with the code DIYSMARTCODE.**
2026-07-22
  • AI tools (computer programs that use artificial intelligence) often struggle with large amounts of information, as performance can degrade after using just 25% of their context window (the amount of data they can process at once).
  • Documentation (instruction manuals) for AI tools is often written for humans, not AI, making it difficult for AI to understand and use effectively without additional context engines (tools that help AI understand information better).
  • In the future, AI tools may focus on creating a "fluency mode" (a smooth, easy-to-use experience) rather than a "friction mode" (making it hard for users to switch to a different tool), with skills (specialized abilities) playing a key role in this shift.
  • When evaluating AI platforms, users often consider factors like security, data governance (how data is managed and protected), and integration (how well it works with other systems) capabilities.
2026-07-13
  • Claude Code (a tool for building AI-powered automations) lets you work with local files and online services like Gmail, Slack, or a CRM (customer relationship management system), making it more powerful than Claude Chat (a simple AI chatbot).
  • Claude Code uses the same AI models (like Opus, Sonnet, or Haiku) as Claude Chat, but adds extra features for working with files and online services.
  • Claude Code is like an AI harness (a tool that helps you use AI models), which sits between the AI model (the engine) and you (the driver), helping you build automations and agents (AI systems that can do tasks for you).
  • The instructor, Nate, uses Claude Code to build and manage multiple businesses, showing how one person can do the work of a team with AI.
2026-07-07
  • MCP (Model Context Protocol) apps are a newer way to build software that lets users interact with AI tools through simple, one-click installs, and they've been around since late 2025.
  • MCP apps can now return UI (user interface) elements, like buttons or forms, instead of just JSON (a common data format), making interactions more engaging and user-friendly.
  • Stores from companies like Character AI and Cloud have opened up, allowing developers to submit their MCP apps for wider distribution and easier user access.
  • Tools like Manufact's open-source SDKs (software development kits) and Manifold Cloud help developers build, test, and share MCP servers and apps more efficiently.
2026-07-01
  • Google recently made a new standard called the Open Knowledge Format, which is a simple folder of text files that AI can use to store and recall information, replacing a more complex and expensive database method.
  • Andrej Karpathy, a well-known AI expert, introduced an idea called the LLM Wiki, where AI builds and maintains a living encyclopedia of linked text files, doing the summarizing and organizing work automatically.
  • The Open Knowledge Format is easy to use and scale, as it allows AI to quickly find relevant information without processing the entire library each time, and it can be used offline without needing a database or server.
  • However, there are some catches: the format doesn't automatically update information, AI can make mistakes in formatting and linking, and the meaning of the information is not standardized, leaving teams to agree on what is inside the container.
2026-06-25
  • Claude managed agents (AI workers that do tasks for you) can now run on their own schedule and safely use real passwords or API keys (secure codes that let software talk to each other), making them more useful for real work.
  • These agents act like employees, using your tools like email or Slack (a messaging app for teams) to complete tasks you describe in plain English, without needing you to set up complex workflows.
  • Claude has improved by allowing agents to run automatically and securely store sensitive information, addressing two major limitations that previously made AI agents feel like just demos or toys.
  • The service offers different AI models (Opus for complex tasks, Sonnet for everyday work, and Haiku for quick, cheap results) and a simple console (dashboard) to manage agents, view credits, and handle billing.
2026-06-13
  • PostHog (a company that makes tools to help you understand how people use your software) is working on a system that automatically fixes problems in your product using data from PostHog's tools.
  • Instead of waiting for you to notice issues on dashboards, PostHog wants to use a background agent (a program that runs quietly in the background) to detect problems and create pull requests (a way to propose changes to code) automatically.
  • The system ingests (takes in) trillions of events per month, groups them, and uses a research agent (a program that investigates and understands the problems) to figure out what's wrong and where the problem is in the code.
  • To handle the huge amount of data and noise, PostHog uses a safety filter (a program that checks for harmful or fake data) and normalizes (makes consistent) the data before grouping similar problems together.
2026-06-07
  • **MCP (Multi-Context Protocol)** is a new open standard that helps apps like VS Code (a free code editor from Microsoft) share information with AI tools (called LLMs, or large language models) to make them smarter and more interactive.
  • MCP apps now let AI tools return interactive elements like diagrams or buttons directly in the chat, making it easier to explore data or even shop online without leaving the chat.
  • MCP uses three main parts: hosts (like VS Code), clients (like GitHub Copilot, a tool that helps write code), and servers (lightweight programs that provide specific functions or data).
  • To keep things safe, it's best to use MCP servers recommended by VS Code or GitHub, as random internet servers might have security issues or harmful content.
2026-06-04
  • AI models like GPT 5.2 (a new version of ChatGPT) and Opus 4.5 can now write fully working website code from a single request, sometimes better than human developers.
  • Instead of forcing every app to add a chat window, new MCP apps (a system that lets one AI app control other tools) create a single super app to handle all your tasks in one place.
  • Developers now use "generative UI" (AI creating user interfaces on the fly) and declarative UI (predefined components AI fills with data), moving beyond old static designs that don't change.
2026-06-03
  • MCP (a tool that connects to outside apps like GitHub) is best for external services; skills (custom instruction packages) work for your team's own processes.
  • Skills load only what's needed instead of everything at once, saving your AI tool from wasting processing power and money.
  • Connecting MCP to outside services like GitHub, Slack, or Figma takes just one command, even though hundreds are available.

Key points

What it is

  • **MCP** (Anthropic's backend protocol) is a system that lets AI agents talk to serverside tools like databases or email using JSON RPC (a way for programs to communicate via text).
  • **WebMCP** (Google's frontend protocol) is a browser tool that connects AI agents directly to web pages, making interactions more accurate and efficient.

How to use it

  • Use **MCP** when you need an AI agent to access backend tools, like databases or APIs, by installing an MCP server.
  • Use **WebMCP** for AI agents to interact with live websites, like searching products or checking out, without needing to set up a server.

Watch out for

  • Don't treat MCP and WebMCP as interchangeable; they serve different purposes and layers of the stack.
  • Avoid overcomplicating setup; use simple commands or config files to add MCP servers, and consider token efficiency for simpler tasks.

Tools named

  • Claude Code (an AI assistant for coding tasks), GitHub (a platform for software development), PostgreSQL (a database management system), Sentry (an error monitoring tool), MCP (Anthropic's backend protocol), WebMCP (Google's frontend protocol)

Lesson 1: What is Difference Between MCP and WebMCP and why it matters

MCP vs. WebMCP: Why the Difference Matters

MCP (Model Context Protocol) is Anthropic's backend protocol. It uses JSON RPC over standard IO to connect AI platforms to server-side tools. Think of it as a structured way for an AI agent to talk to a database, a file system, or an external API. WebMCP is Google's front-end protocol — a browser-native API that lets AI agents interact directly with web pages. While MCP handles the server, WebMCP handles the browser.

They are complementary, not competing. Together they form the "full stack of AI tool integration" — backend and frontend, server and browser. WebMCP solves a massive accuracy problem. Without it, AI agents interact with websites like "a blindfolded person uses a vending machine": taking screenshots, scraping the DOM, and guessing pixel-by-pixel for roughly 70% accuracy. WebMCP turns any website into a structured AI tool with 98% accuracy and 89% fewer errors. It's a W3C standard co-authored by Google and Microsoft, adding a JavaScript API (`navigator.modelContext`) that lets sites expose callable tools to agents like Claude, ChatGPT, or Gemini. The browser sits in the middle as a trust layer, enforcing permissions and consent.

For AI development, this distinction matters because it clarifies where to build. If you need an agent to query a backend service (a database, an API), you build an MCP server. If you need an agent to interact with a live website (searching products, checking out, running a code review), you use WebMCP. Ignoring the difference means either building broken browser automation or missing the efficiency of structured server-side tools.

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Lesson 2: How to use Difference Between MCP and WebMCP: step-by-step

MCP and WebMCP are not the same thing — they are complementary protocols (systems for connecting software). MCP is Anthropic's backend protocol. It uses JSON RPC over standard IO (a way for programs to talk via text) to connect AI agents to serverside tools like databases or email. WebMCP is Google's frontend protocol. It is a browser-native API (a tool built into the browser) that connects agents directly to web pages on the client side. Together, they form the full stack of AI tool integration: server and browser, backend and frontend.

To use MCP, you install an MCP server — essentially a toolbox that gives your agent access to another software's tools. For example, in Claude Code, you type `claude mcp add` followed by the server name and URL. This lets the agent figure out which tools to use, what parameters to fill, and when to use them, so you don't have to think about it. An MCP server is less token-efficient (uses more processing units) than a raw API, but it handles tool selection automatically.

WebMCP works differently. It is not installed as a server. Instead, it is a browser-native protocol that lets agents interact with live web pages — for example, an e-commerce agent that searches, filters, and checks out through structured tools on a website. You do not need to set up an MCP server for WebMCP; it runs in the browser.

Use MCP when you need backend tool access (e.g., GitHub, Postgres). Use WebMCP when your agent needs to interact with frontend web pages directly. They are not interchangeable — they cover different layers of the stack.

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Lesson 3: Best practices and pitfalls

MCP and WebMCP are not the same thing, despite some confusion. MCP (Anthropic’s backend protocol) uses JSON RPC over standard IO to connect AI platforms to serverside tools. WebMCP (Google’s front-end protocol) is a browser native API connecting agents directly to web pages’ backend and frontend. Together, they form the full stack of AI tool integration—server and browser, backend and frontend. A common pitfall is treating them as interchangeable. They complement each other but serve different layers: MCP handles server-side actions (e.g., connecting to GitHub, PostgreSQL, or a CRM), while WebMCP gives agents structured access to live websites without screenshots or DOM scraping, boosting accuracy from around 70% to 98%.

A mistake is forcing everything into one feature. MCP servers each handle their own specialty—use them to connect external services like GitHub for pull requests or Sentry for error monitoring. They compose and complement, not compete. For new users, best practice is to avoid overcomplicating setup. With Claude Code, you add MCP servers via a single command or by editing a JSON config file scoped globally or per project. If you find yourself repeating instructions, that calls for a skill (a markdown instruction file), not another MCP server.

Also, consider token efficiency. An API endpoint might return a large JSON snippet but can be more token-efficient than an MCP server, which must carry all its tool descriptions for the agent to browse. For simple tasks, a CLI command may be faster and cheaper than spinning up an MCP server. In short, use MCP for backend tool connectivity, WebMCP for browser-native agent actions, and mix them judiciously based on the job.

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