WebMCP vs MCP: What's the Difference and Why It Matters

MCP and WebMCP solve the same fundamental problem — giving AI models structured access to tools and data — but they operate in different environments and serve different use cases. If you're already using MCP servers in your code editor, understanding WebMCP will help you see where the ecosystem is heading.

The Short Version

MCP (Model Context Protocol) connects AI models to tools in local development environments. It's what powers MCP servers in Claude Code, Cursor, VS Code, and other editors.

WebMCP extends the MCP protocol to the browser, allowing AI agents to interact with web pages through structured tools instead of HTML scraping.

Same protocol family, different runtime environments.

MCP: Tools for Your Editor

MCP has been available since late 2024 and has seen rapid adoption among developers. Here's how it works in practice:

You install an MCP server (like @supabase/mcp-server-supabase), add it to your editor's config file, and your AI assistant gains access to that server's tools. For Supabase, that means the AI can query your database, manage tables, run migrations, and deploy edge functions — all from within your coding session.

Key Characteristics of MCP

• Local execution: MCP servers run on your machine (or connect to remote APIs from your machine)
• Developer-focused: Designed for use in code editors and development tools
• Manual setup: You configure which servers to connect by editing a JSON config file
• API key authentication: Servers typically authenticate via environment variables containing API keys or tokens
• Two transports: stdio (local process) or HTTP (remote server)

What MCP Looks Like

A typical MCP configuration in Cursor:

{
  "mcpServers": {
    "supabase-mcp": {
      "command": "npx",
      "args": ["-y", "@supabase/mcp-server-supabase"],
      "env": {
        "SUPABASE_ACCESS_TOKEN": "sbp_your_token_here"
      }
    },
    "github-mcp": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_your_token_here"
      }
    }
  }
}

Each server entry declares a command to run, arguments, and environment variables for authentication.

WebMCP: Tools for the Browser

WebMCP takes the same protocol concepts and adapts them for the web platform. Instead of configuring servers manually, AI agents discover available tools automatically by visiting websites.

Key Characteristics of WebMCP

• Browser execution: Runs in the browser context, mediated by the browser's security model
• Everyone-focused: Works for any user browsing the web, not just developers
• Automatic discovery: Websites advertise their tools via a manifest file at a well-known URL
• Session authentication: Uses the browser's existing login session — no separate API keys
• HTTP only: Communication happens over standard HTTP, within the browser's fetch API

What WebMCP Looks Like

A website supporting WebMCP would include a manifest:

{
  "name": "Example Dashboard",
  "tools": [
    {
      "name": "getMetrics",
      "description": "Retrieve dashboard metrics for a date range",
      "inputSchema": {
        "type": "object",
        "properties": {
          "startDate": { "type": "string", "format": "date" },
          "endDate": { "type": "string", "format": "date" }
        },
        "required": ["startDate", "endDate"]
      }
    },
    {
      "name": "exportReport",
      "description": "Export a report as CSV or PDF",
      "inputSchema": {
        "type": "object",
        "properties": {
          "format": { "type": "string", "enum": ["csv", "pdf"] }
        }
      }
    }
  ]
}

An AI agent visiting this site could call getMetrics or exportReport without needing to understand the page's HTML structure.

Side-by-Side Comparison

When to Use Which

Use MCP When

• You're developing software and want AI assistance with specific tools
• You need deep integration with databases, APIs, or infrastructure
• You want persistent tool access across your coding sessions
• You're comfortable managing config files and API keys

This is MCP's sweet spot. If you're writing code and want your AI assistant to interact with Supabase, GitHub, Stripe, or any other service, MCP servers are the way to go.

Use WebMCP When

• You want AI agents to help with tasks on websites
• You need the AI to work with your existing web authentication
• The website supports WebMCP
• You don't want to manage API keys or server configurations

WebMCP will shine for non-developer use cases: an AI helping you navigate a complex SaaS dashboard, filling out forms, extracting data from reports, or automating repetitive web workflows.

Why WebMCP Doesn't Replace MCP

You might wonder: if WebMCP works through the browser, why not just use it for everything?

A few reasons:

1. Performance: MCP servers run locally with direct API access. There's no browser overhead, no DOM rendering, no network round-trips to a web page. For development workflows where speed matters, local MCP servers are faster.

2. Capabilities: MCP servers can do things that aren't possible through a browser — running local commands, accessing the filesystem, managing Docker containers, interacting with hardware. WebMCP is limited to what the website exposes.

3. Availability: MCP works today across multiple editors. WebMCP is still a proposal. Even after browser support ships, it will take time for websites to adopt the standard.

4. Control: With MCP, you choose exactly which servers to run and how they're configured. WebMCP tools are defined by the website owner, not you.

The two protocols are complementary. MCP handles your development environment. WebMCP handles the broader web.

The Bigger Picture

MCP and WebMCP are both part of a larger trend: giving AI models structured, permissioned access to tools and data instead of expecting them to work from raw text alone.

In 2024, the question was "how do we get AI models to use tools?" MCP answered that for development environments. In 2026, the question is becoming "how do we extend this to the entire web?" WebMCP is the answer being proposed.

For developers, the practical advice is straightforward:

1. Use MCP now: Set up MCP servers in your editor. The productivity gains are real and immediate. Check out our curated stacks for a quick start.

2. Watch WebMCP: Follow the specification development. If you build web applications, start thinking about which tools you'd want to expose.

3. Build on the protocol: The concepts — tool discovery, structured schemas, permissioned access — are consistent across MCP and WebMCP. Time spent understanding one directly applies to the other.

The MCP ecosystem is growing fast. Whether through your editor or your browser, structured tool access is becoming the standard way AI models interact with the world.