What Is MCP? A Developer's Guide to the Model Context Protocol

The Model Context Protocol (MCP) is an open protocol that lets AI coding tools access external data sources and tools through a standardised interface. Instead of building a separate integration for every AI tool, you build one MCP server and every MCP-compatible tool can use it.

For developers using AI tools in their workflow, MCP is the missing standard that makes tool integration predictable.

What MCP is

MCP defines a communication standard between AI assistants (clients) and the tools and data sources they need to access (servers).

Before MCP: if you wanted Claude Code to access your database, Cursor to access your docs, and another AI tool to access your analytics, you needed three separate custom integrations.

After MCP: one MCP server exposes your data. Any MCP-compatible client can connect to it.

The protocol covers:

How clients discover what capabilities a server offers
How clients call tools and read resources
How servers respond with structured data

It is a JSON-RPC-based protocol running over stdio or HTTP+SSE.

How it works

An MCP server exposes three types of capabilities:

Tools — Functions the AI can call. For example: get_visitors(date_range) or get_top_pages(site_id, limit).

Resources — Read-only data the AI can access. For example: a live analytics summary, or a list of your sites.

Prompts — Pre-built prompt templates that use your data. For example: "Summarise this week's traffic" filled with your actual numbers.

When you ask your AI tool "how many visitors did my site get yesterday?", the AI:

Identifies that your MCP server has a get_visitors tool
Calls the tool with appropriate parameters
Receives the data
Incorporates it into its response

Which tools support MCP

MCP is supported and growing. Check the current documentation for each tool to confirm support:

Claude Code — MCP server support
Cursor — MCP server configuration
Cline — MCP integration
Windsurf — MCP support

The MCP ecosystem is expanding. For the current list of supported clients, see the MCP documentation.

Why analytics + MCP makes sense

Your analytics data is useful in context. When you are writing code that changes a feature, it is useful to know how many people use that feature. When you are deciding what to build next, it is useful to know which pages get the most traffic.

An Antlytics MCP server means you can ask questions like:

"Which pages on my site have the highest traffic this month?"
"What are the top referrers for my blog?"
"How many visitors have I had since I launched the new feature?"

...and get answers without leaving your editor or opening a dashboard.

See the AI coding tools documentation for setup instructions once the Antlytics MCP server is available.

FAQ

Do I need to be a developer to use MCP? You need to configure MCP settings in your AI tool. It is a developer feature, not a no-code tool.

Is MCP a standard or a product? It is an open protocol maintained by Anthropic and the community. Multiple companies are building MCP servers for their products.

Does Antlytics have an MCP server? The Antlytics MCP server is in development. See the roadmap for current status.

Can I build my own MCP server for Antlytics? Yes. The Antlytics API is publicly documented at /docs/api-reference. You can build an MCP server that wraps the API.