GSC + SERP Data: Combined SEO Workflow via MCP

Data source | Provides                    | Misses
GSC         | Clicks, impressions, CTR    | Competitor data, AI Overviews
SERP API    | Rankings, competitors, AIO  | Your click/impression data
Combined    | Full picture                | Nothing material

{
  "mcpServers": {
    "scavio": {
      "url": "https://mcp.scavio.dev/mcp",
      "headers": {
        "x-api-key": "YOUR_KEY"
      }
    }
  }
}

import requests

# Step 1: Get your top queries from GSC
# (This data comes from GSC API or exported CSV)
gsc_queries = [
    {"query": "search api for ai agents", "clicks": 45, "impressions": 1200, "ctr": 0.038},
    {"query": "serp api pricing comparison", "clicks": 30, "impressions": 800, "ctr": 0.038},
    {"query": "tavily alternatives 2026", "clicks": 20, "impressions": 600, "ctr": 0.033},
]

# Step 2: Enrich with live SERP data
def enrich_gsc_query(gsc_data: dict) -> dict:
    resp = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": "YOUR_KEY"},
        json={
            "query": gsc_data["query"],
            "num_results": 20,
            "include_ai_overview": True
        }
    )
    serp = resp.json()

    # Find your rank
    my_domain = "yourdomain.com"
    rank = None
    for r in serp.get("organic_results", []):
        if my_domain in r.get("url", ""):
            rank = r["position"]
            break

    # Check AI Overview
    ai_cited = any(
        my_domain in c.get("url", "")
        for c in serp.get("ai_overview", {}).get("citations", [])
    )

    # Get competitors above you
    competitors_above = [
        r["url"] for r in serp.get("organic_results", [])
        if r["position"] < (rank or 999) and my_domain not in r["url"]
    ]

    return {
        **gsc_data,
        "current_rank": rank,
        "ai_overview_cited": ai_cited,
        "competitors_above": competitors_above[:3],
        "has_ai_overview": bool(serp.get("ai_overview"))
    }

enriched = [enrich_gsc_query(q) for q in gsc_queries]
for e in enriched:
    print(f"Query: {e['query']}")
    print(f"  GSC: {e['clicks']} clicks, {e['impressions']} impressions")
    print(f"  Rank: #{e['current_rank']}, AI Cited: {e['ai_overview_cited']}")
    print(f"  Competitors above: {e['competitors_above']}")

// Identify optimization opportunities
function analyzeOpportunities(enrichedQueries) {
  const opportunities = [];

  for (const q of enrichedQueries) {
    // High impressions, low CTR, not in AI Overview
    if (q.impressions > 500 && q.ctr < 0.03 && !q.ai_overview_cited) {
      opportunities.push({
        query: q.query,
        type: "AEO optimization",
        reason: "High visibility but low CTR -- AI Overview citation could help"
      });
    }

    // Ranking 4-10 with AI Overview existing
    if (q.current_rank >= 4 && q.current_rank <= 10 && q.has_ai_overview) {
      opportunities.push({
        query: q.query,
        type: "Content improvement",
        reason: "Rank " + q.current_rank + " -- improve content to get AI citation"
      });
    }

    // Ranking 1-3 but losing clicks (AI Overview taking clicks)
    if (q.current_rank <= 3 && q.ctr < 0.02 && q.has_ai_overview) {
      opportunities.push({
        query: q.query,
        type: "AI Overview defense",
        reason: "Top 3 rank but low CTR -- AI Overview absorbing clicks"
      });
    }
  }

  return opportunities;
}