TL;DR: seo-performance-mcp pulls Google Search Console, GA4, Matomo, Clarity, and AI-citation data per URL and emits a verdict per post (refresh, expand, merge, kill, double_down, hold) with explicit reason codes.
We just published @automatelab/seo-performance-mcp on npm. It is a Model Context Protocol server for the part of SEO that starts after the post goes live: triage. Which URLs are decaying, which are quietly winning, which lost an AI citation last week, which sit at position 7 with a low CTR and want a title rewrite. Free, MIT-licensed, no email gate.
What does seo-performance-mcp do?
Most SEO tooling stops at "the post is live." This MCP starts there. For any URL on your site it pulls Search Console (clicks, impressions, CTR, position, top queries), GA4 (pageviews), Matomo (visits, dwell), Microsoft Clarity (scroll depth, rage clicks), and AI-citation counts from a configured citation-intelligence endpoint. Every signal source is optional. Provide the env vars for the platforms you use; the rest are skipped silently.
The data flows through a deterministic verdict engine. It combines the snapshot with a 12-week GSC decay curve and emits one of six calls per post: refresh, expand, merge, kill, double_down, or hold. Each verdict carries explicit reason codes (the rules live in src/verdict/rules.ts and can be inspected) and a 0-1 confidence score. The mapping is rules-based, not LLM-based, so the same inputs always produce the same call.
Reporting only. The server never edits a post, never publishes anything, never touches a third-party platform with anything other than a read. The verdict and the refresh brief are hand-off artefacts for a writer, an editor, or a downstream rewrite tool.
Which tools does it ship?
Eight tools, organised as a dot-notation tree: posts.* for per-URL analysis, cohort.* for cross-post roll-ups, gsc.* for direct Search Console scans.
-
posts.list: Discover indexable posts via an XML sitemap, a JSON override list, or the Ghost Admin API. Filter by minimum age or published-after date. -
posts.snapshot: Pull a 30, 60, or 90-day snapshot across every configured signal source for one URL. Each source is best-effort. -
posts.decay_curve: Bucket GSC clicks, impressions, and average position into weekly windows and classify the trend: decay, plateau, or growth. Underpins the verdict engine's decay rules. -
posts.verdict: Run the rule-based verdict engine on one URL. Returns verdict, reason codes, and confidence. -
posts.refresh_brief: Produce a markdown brief for a human or downstream LLM editor: verdict, reasons, raw numbers, top queries, suggested actions. -
posts.cite_loss: Return the list of LLMs that previously cited this URL but no longer do, with the prior query and last-seen date. Optionally identifies the URL that replaced yours. -
cohort.report: Run the verdict engine across a cohort filtered by tag and minimum age. Returns a ranked table sorted by verdict priority then confidence. Answers "which three posts should I refresh this week?" -
gsc.quick_wins: Scan GSC for (page, query) pairs at positions 5 to 15 with non-trivial impressions and a CTR below the position-expected curve. The fastest title-rewrite wins. Platform-agnostic pure GSC pull.
Three discoverable prompts ship alongside: audit_cohort, find_quick_wins, citation_loss_sweep. No skill loader required; any MCP-capable client picks them up.
Install
Requires Node 20+. Add the server to your MCP host config (~/.cursor/mcp.json for Cursor, claude_desktop_config.json for Claude Desktop, .mcp.json for Claude Code), then restart the host.
json
{
"mcpServers": {
"seo-performance": {
"command": "npx",
"args": ["-y", "@automatelab/seo-performance-mcp"],
"env": {
"GSC_SERVICE_ACCOUNT_JSON": "/abs/path/to/gsc-service-account.json",
"GSC_SITE_URL": "sc-domain:example.com",
"MATOMO_BASE_URL": "https://example.com/analytics",
"MATOMO_TOKEN": "...",
"MATOMO_SITE_ID": "1",
"GA4_PROPERTY_ID": "123456789",
"CLARITY_PROJECT_ID": "...",
"GHOST_ADMIN_KEY": "id:secret"
}
}
}
}
