What is MCP?

The Model Context Protocol (MCP) is an open standard that enables AI models to securely interact with local and remote resources. It provides a universal way to connect Large Language Models (LLMs) like Claude to data sources, tools, and prompts.

The Problem

Previously, connecting an AI assistant to a new tool or dataset required building a custom integration for that specific AI application. If you wanted to use the same tool with a different AI, you had to rebuild the integration. This led to fragmented ecosystems and duplicated effort.

The MCP Solution

MCP solves this by defining a standard protocol for:

1. Exposing Resources: Letting the AI read data (files, database rows, API responses).
2. Providing Tools: Letting the AI perform actions (run code, query APIs, update records).
3. Defining Prompts: Providing reusable templates for common tasks.

Architecture

MCP follows a client-server architecture:

• MCP Host: The application where the AI lives (e.g., Claude Desktop, an IDE, or a custom AI app).
• MCP Client: The component within the Host that speaks the protocol.
• MCP Server: A standalone service that exposes specific capabilities (like this PageSpeed Insights server).

graph LR
    A[AI Model] <--> B[MCP Host / Client]
    B <--> C[MCP Server 1]
    B <--> D[MCP Server 2]
    C <--> E[Data/API]
    D <--> F[Local Files]

How This Project Fits In

PageSpeed Insights MCP is an MCP Server.

• It connects to the Google PageSpeed Insights API.
• It translates the API's complex JSON responses into Tools that the AI can understand and use.
• It runs locally on your machine (or in a container) and communicates with your AI assistant (like Claude) via standard input/output (stdio).

When you ask Claude to "Analyze example.com", Claude doesn't know how to talk to Google's API directly. Instead, it sends a request to this MCP server via the protocol. The server handles the API call, processes the data, and returns a structured result that Claude can interpret and explain to you.