Building a Full SEO Research Pipeline with Claude Code

If you’re building an SEO tool, a content management system, or anything that needs to surface keyword and trend data, Claude Code can now do that research directly — without you switching to a browser or a separate SEO platform.

Why This Matters

Building SEO features requires understanding what the data looks like in practice: which queries return heavy competition, how trend signals behave over time, what kind of content dominates for a given keyword. You can’t model that well without looking at real results.

With AgentPatch connected, Claude Code can run that research inline. Ask it to investigate a keyword and it uses Google Search, Google Trends, and Google News together to give you a grounded answer — which it can then turn into code, fixtures, or documentation.

Setup

The AgentPatch CLI is designed for AI agents to use via shell access. Install it, and your agent can discover and invoke any tool on the marketplace.

Install (zero dependencies, Python 3.10+):

pip install agentpatch

Set your API key:

export AGENTPATCH_API_KEY=your_api_key

Example commands your agent will use:

ap search "web search"
ap run google-search --input '{"query": "test"}'

Get your API key from the AgentPatch dashboard.

Install the AgentPatch skill — it teaches Claude Code when to use AgentPatch and how to use the CLI:

/plugin marketplace add fullthom/agentpatch-claude-skill
/plugin install agentpatch@agentpatch

MCP Server (Alternative)

If you prefer raw MCP tool access instead of the skill:

claude mcp add -s user --transport http agentpatch https://agentpatch.ai/mcp \
  --header "Authorization: Bearer YOUR_API_KEY"

Replace YOUR_API_KEY with your actual key from the AgentPatch dashboard.

Example

You’re building an SEO dashboard and want to test the keyword analysis feature with real data. You tell Claude Code:

“Research the keyword ‘serverless database’ — check what’s ranking, current trends, and recent news. I want to understand the data shape before I design the analysis component.”

Claude Code runs the pipeline:

Searching Google for "serverless database"...
Checking Google Trends for "serverless database"...
Searching Google News for "serverless database"...

Results:

Search: Top results are dominated by PlanetScale, Neon, Turso, and Supabase
comparison posts. Documentation pages rank alongside "best of" listicles.
Average result has 8+ backlinks from dev-focused domains.

Trends: Steady upward trend over 24 months. Spike in October 2025 correlating
with a Cloudflare D1 announcement. Currently 40% above baseline from 12 months ago.

News: Recent coverage is mostly product announcements and pricing changes.
PlanetScale's pricing controversy generated the most links in the last 90 days.

Based on this, here's the data structure I'd suggest for the KeywordAnalysis component:

interface KeywordAnalysis {
  keyword: string;
  trendDirection: 'up' | 'down' | 'flat';
  trendPercent: number;
  topCompetitors: string[];
  recentNews: { title: string; source: string; date: string }[];
  contentType: 'informational' | 'commercial' | 'mixed';
}

Claude Code researched the keyword and turned the findings directly into a component interface. That’s a more grounded design than guessing at the data shape.

Wrapping Up

With AgentPatch connected, Claude Code can run SEO research inline during any coding session. Google Search, Trends, and News are three tools — the same config also unlocks Maps, email, image generation, and everything else on the marketplace. Explore at agentpatch.ai.

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