From MCP to WebMCP: Both Sides of the AI Development Coin

Two protocols share the same family name, target completely different audiences, and together define how AI fits into the web development lifecycle from end to end.

MCP is for developers building with AI. WebMCP is for websites serving AI agents. If you build web applications, you will use both -- MCP in your editor while you write the code, WebMCP on your deployed site so AI agents can interact with it. Understanding how they connect is the key to thinking clearly about where AI tooling is heading.

MCP: The Developer Side

The Model Context Protocol connects your AI coding assistant to databases, APIs, and external tools. You configure a JSON file in your editor, point it at one or more MCP servers, and your assistant gains structured access to those services.

The setup is straightforward. Add a server to your config, provide an API key, and the AI can call that server's tools directly. Supabase MCP lets Claude run queries, manage migrations, and deploy edge functions -- all from your editor. GitHub MCP lets it create branches, open pull requests, and review code. Context7 MCP gives it access to up-to-date documentation for any library you are working with.

There are over 17,000 MCP servers available today. But most developers only need 3-5 for their daily workflow. The ecosystem is mature and production-ready, with support across Claude Code, Cursor, VS Code Copilot, Windsurf, and other clients.

The main challenge is not finding servers -- it is choosing the right ones. Too many servers means token bloat that eats your context window and degrades performance. A curated stack of 3-4 well-chosen servers consistently outperforms a random collection of 10.

WebMCP: The User Side

WebMCP flips the direction. Instead of connecting an AI to tools inside your editor, it lets your deployed website expose structured tools to AI agents that visit it.

The specification was published as a W3C Community Group report on February 10, 2026. It defines two approaches for websites to declare their tools:

Declarative: HTML attributes on existing form elements that describe what each form does in terms an AI agent can understand. This requires minimal code changes -- you annotate your existing markup, and agents can discover and use your forms without scraping.

Imperative: A JavaScript API (navigator.modelContext) that lets you register tools programmatically. This is more powerful and supports complex interactions that go beyond simple form submissions -- things like multi-step workflows, real-time data queries, and conditional logic.

WebMCP is early. It runs only in Chrome 146 Canary today, with stable browser support expected in mid-to-late 2026. But early adopters are already building: Plotono has exposed 85 tools via WebMCP, a WordPress plugin exists for declarative adoption, and the MCP-B polyfill lets you experiment without waiting for native browser support.

The core value proposition is clear. Instead of AI agents scraping your site's HTML and guessing at its structure, they call typed functions with defined inputs and outputs. It is the difference between a human reading a restaurant menu and an API returning the dish list as JSON.

The Connection

Here is where these two protocols meet. They are not competing. They are not even alternatives. They are two halves of the same workflow.

The workflow goes like this: you use MCP servers in your editor to build your website. Then you add WebMCP to that website so AI agents can use it. Same protocol family, both sides of the same coin.

This is not hypothetical. It is already happening with the tools available today.

The Full Cycle: Three Examples

Ecommerce Store

Build with MCP: You set up Supabase MCP to manage your product database and Stripe MCP to handle payment integration. Your AI assistant creates tables, writes queries, sets up webhook handlers, and configures checkout flows -- all from within your editor.

Deploy with WebMCP: Your live storefront exposes tools for product search, add-to-cart, and checkout initiation. An AI shopping agent visiting your site can query your catalog with filters, add items to a cart, and start checkout -- all through typed function calls instead of scraping product cards and clicking buttons.

Documentation Site

Build with MCP: You use Context7 MCP to pull in reference docs for your framework and Filesystem MCP to manage your content files. The AI helps you write, organize, and cross-reference your documentation without leaving the editor.

Deploy with WebMCP: Your published docs site exposes a search tool via WebMCP. When an AI coding assistant needs to look something up in your docs, it calls your search tool directly and gets structured results -- titles, URLs, relevant snippets -- instead of scraping your search results page.

SaaS Dashboard

Build with MCP: You use Postgres MCP to manage your database schema and Sentry MCP to monitor errors during development. The AI helps you write migrations, optimize queries, and triage bugs from your editor.

Deploy with WebMCP: Your dashboard exposes tools for data export and report generation. A user's AI assistant can visit the dashboard, call exportMetrics with a date range and format, and receive a clean CSV -- no browser automation, no screenshot parsing, no guesswork.

In each case, MCP accelerates the building. WebMCP makes the result accessible to AI agents. The two protocols bookend the development lifecycle.

Timeline and Strategy

MCP: Use It Now

MCP is production-ready and battle-tested. The ecosystem has 17,000+ servers, broad client support, and a growing community of developers who rely on it daily.

If you have not set up MCP servers in your editor, start with a curated stack that matches your workflow. A frontend developer needs different servers than a data engineer. A solo indie hacker needs different servers than a team lead at an enterprise. The right 3-5 servers will save you hours every week.

WebMCP: Prepare Now, Deploy When Stable

WebMCP is not ready for production, but that does not mean you should ignore it. The specification is public, the mental model is clear, and there are zero-risk steps you can take today.

Start with declarative attributes. Adding WebMCP annotations to your existing HTML forms costs nothing and breaks nothing. Browsers that do not support WebMCP simply ignore the attributes. When support ships in stable Chrome, your forms will be agent-ready without any additional work.

Plan your imperative tools. Think about which parts of your application would benefit from AI-agent access. What would an agent need to do on your site? Search your content? Export data? Initiate a workflow? Sketch out the tool schemas now so you are ready to implement when the API stabilizes.

Experiment with the polyfill. The MCP-B polyfill lets you test WebMCP patterns today without waiting for native browser support. It is a low-commitment way to build intuition for how the API works.

Where to Go From Here

MCP and WebMCP are not competing standards. They are complementary layers of the same ecosystem -- one for building, one for serving.

For the MCP side, browse our curated stacks to find the right servers for your workflow. Each stack is optimized for a specific role with servers that complement each other without overlapping.

For the WebMCP side, read our deep dives on what WebMCP is and how it compares to MCP at the protocol level.

The developers who will move fastest over the next year are the ones who understand both sides of this coin: using MCP to build faster today, and preparing their applications for a world where AI agents are first-class users of the web.