What is MCP (Model Context Protocol)?

The problem MCP solves

A language model by itself can only work with what is in its context window — the messages you have sent, plus whatever was in its training data. It cannot read a file on your computer. It cannot query a live database. It cannot call an external API. It generates text based on what it already knows, which means it cannot access real-time or proprietary information without help.

Tools solve that. When you give Claude a tool — say, "read this file" or "run this SQL query" — the model can call that tool as part of generating a response, use the result, and continue. This is what makes AI agents genuinely useful for real-world tasks rather than just text generation.

Before MCP, every tool integration was bespoke. Building a tool for Claude required Claude-specific code. Building the same tool for ChatGPT required a separate ChatGPT plugin. For Cursor, a Cursor extension. Each AI client had its own format, its own connection method, its own SDK. Teams building AI applications had to maintain parallel implementations for every model they supported.

MCP collapses that. One server, one protocol, works across every client that speaks MCP. The same MCP server that works in Claude Desktop works in Cursor, Windsurf, VS Code with GitHub Copilot, and any future client that adopts the standard.

How MCP works

MCP defines three types of primitives that a server can expose:

MCP transport: stdio vs HTTP

MCP servers connect to clients over one of two transports:

MCP vs alternatives

The MCP ecosystem in 2026

MCP passed 1 billion server downloads in 2026 — faster adoption than npm in its first years. The protocol is now supported natively by:

The open-source MCP server registry has thousands of servers covering databases (PostgreSQL, SQLite, MongoDB), developer tools (GitHub, Linear, Jira), knowledge bases (Notion, Confluence), communication (Slack, Gmail), and infrastructure (AWS, GCP, Kubernetes).

How to connect to an MCP server

For Claude Desktop, Cursor, and most stdio clients, add the server to your mcpServers configuration:

{
  "mcpServers": {
    "agentready": {
      "command": "npx",
      "args": ["-y", "@agentreadyweb/mcp"]
    }
  }
}

For clients that support HTTP transport (WebMCP), connect directly by URL — no install required:

https://www.agentready.it.com/api/mcp

MCP and websites: the access gap

One of the largest remaining gaps in the MCP ecosystem is reliable web access. AI agents that need to query a website typically fall back to web_fetch — a direct HTTP request that fetches raw HTML. This fails for three common reasons:

• ✗JavaScript-rendered content — React, Next.js, and Vue sites return an empty HTML shell. web_fetch fetches the shell and misses all the content.
• ✗Multi-page answers — If the answer spans pricing, docs, and an FAQ page, web_fetch can only fetch one URL per call.
• ✗No citations — web_fetch returns raw text with no attribution. The model has no way to tell the user where information came from.

AgentReady's ask_site MCP tool solves this. It indexes the full site (handling JS rendering, multi-page crawling, and content chunking), stores it in a vector database, and retrieves cited answers across all pages. On a benchmark of 60 real-world test cases, ask_site achieved +20 percentage points of fact coverage over web_fetch on JavaScript-heavy sites, with 100% citation rate versus 0%.

Frequently asked questions