Your website generates data constantly — crawl results, content health metrics, Lighthouse performance scores, search visibility trends, traffic patterns. That data lives in dashboards, spreadsheets, and tool-specific interfaces. Your AI coding assistant — Claude Code, Cursor, Claude Desktop — can't see any of it.
Model Context Protocol (MCP) fixes that gap. MCP is the specification that lets AI applications call external tools and retrieve structured data during a conversation. When Evergreen's MCP Server is connected to Claude Code, the AI can query your site's audit data, analyze it, cross-reference it with traffic data, and generate actionable recommendations — all without leaving the terminal.
This isn't theoretical. It's a working integration today, and it changes how developers interact with SEO data.
Why website intelligence needs an AI interface
Traditional website intelligence tools — crawlers, audit platforms, analytics dashboards — present data through visual interfaces designed for human consumption. That's fine when a human is doing the analysis. It breaks down in two scenarios:
Developer workflows. A developer maintaining a Next.js site doesn't want to open a separate dashboard to check whether their latest deployment broke any meta descriptions. They want to ask Claude Code "did anything change?" and get an answer in the same terminal where they're writing code.
Scale. A 5,000-page site has 5,000 title tags, meta descriptions, Lighthouse scores, and internal link counts. A human scanning a dashboard can spot patterns in a few hundred rows. An AI agent connected to structured data can analyze all 5,000 pages, correlate issues with traffic data, and prioritize by impact in seconds.
MCP makes both scenarios work by exposing website intelligence as queryable tools rather than visual dashboards. The data is the same. The interface is different — and the different interface enables different workflows.
What Evergreen's MCP Server exposes
Evergreen's MCP Server makes your site's data available as structured tools that AI clients can discover and call:
- Audit summary — total pages, issue counts, score distributions
- Pages with issues — filtered by issue type (missing metadata, broken links, noindex conflicts, performance problems)
- Lighthouse data — per-page scores, Core Web Vitals, worst performers
- Site structure — page hierarchy, depth, internal link topology
- Search visibility — GSC impressions, clicks, average position (when connected)
- Traffic data — GA4 sessions, engagement metrics (when connected)
Each tool returns structured data that the AI can parse, analyze, and act on. The tools are designed for AI consumption — they return filtered, actionable data rather than raw dumps.
The guides in this section
This section covers the practical workflows and concepts behind AI-assisted website intelligence:
Claude Code SEO audit: the developer's playbook
A hands-on walkthrough of configuring Evergreen's MCP Server in Claude Code and running your first AI-assisted audit. Includes configuration steps, sample queries, a full worked example, and honest coverage of the limits. Start here if you want to try it today.
Agentic SEO workflows: how AI agents transform site audits
A category-defining explainer on what "agentic SEO" actually means — multi-step, tool-connected, data-driven investigation — versus the hype. Covers three worked examples (weekly health checks, content decay investigation, pre-deploy checks), the real limits (hallucinations, context windows, tool reliability), and where the data layer matters most.
The institutional memory argument
You can vibe-code a crawler. You can't vibe-code institutional memory.
An ad-hoc script that Claude Code writes to crawl your site gives you a one-time snapshot. Evergreen's MCP Server gives the AI access to continuously maintained, historically tracked, cross-correlated website intelligence — the difference between a one-time consultant and an analyst who knows your site's history.
That persistent data layer is what makes agentic workflows genuinely useful rather than a novelty. The AI can compare today's data to last week's. It can correlate traffic drops with content changes. It can track whether a fix actually improved the metrics it was supposed to improve. None of that works with ad-hoc data.
Getting started
The fastest path from zero to a working MCP integration:
- Sign up for Evergreen (free plan, 1 project, 500 pages)
- Crawl your site (takes about two minutes for a 500-page site)
- Upgrade to Pro ($49/mo) for MCP Server access
- Configure the MCP Server in Claude Code (step-by-step guide)
- Ask your first question — "What are the top issues on my site?"
The entire setup takes under 10 minutes. The workflow change lasts.
Connect Evergreen to Claude Code in two minutes → Start free
