Google Search Console

Pull search performance data, index coverage, and Core Web Vitals from Google Search Console API.

Prerequisites

Requires Google OAuth credentials:

• GOOGLE_CLIENT_ID
• GOOGLE_CLIENT_SECRET
• A valid OAuth access token with https://www.googleapis.com/auth/webmasters.readonly scope

Set credentials in .env, .env.local, or ~/.claude/.env.global.

Getting an Access Token

# Step 1: Authorization URL (user visits in browser)
echo "https://accounts.google.com/o/oauth2/v2/auth?client_id=${GOOGLE_CLIENT_ID}&redirect_uri=urn:ietf:wg:oauth:2.0:oob&scope=https://www.googleapis.com/auth/webmasters.readonly&response_type=code&access_type=offline"

# Step 2: Exchange code for tokens
curl -s -X POST "https://oauth2.googleapis.com/token" \
  -d "code={AUTH_CODE}" \
  -d "client_id=${GOOGLE_CLIENT_ID}" \
  -d "client_secret=${GOOGLE_CLIENT_SECRET}" \
  -d "redirect_uri=urn:ietf:wg:oauth:2.0:oob" \
  -d "grant_type=authorization_code"

# Step 3: Refresh expired token
curl -s -X POST "https://oauth2.googleapis.com/token" \
  -d "refresh_token={REFRESH_TOKEN}" \
  -d "client_id=${GOOGLE_CLIENT_ID}" \
  -d "client_secret=${GOOGLE_CLIENT_SECRET}" \
  -d "grant_type=refresh_token"

Listing Available Sites

curl -s -H "Authorization: Bearer ${GSC_ACCESS_TOKEN}" \
  "https://www.googleapis.com/webmasters/v3/sites" \
  | python3 -c "
import json, sys
data = json.load(sys.stdin)
for site in data.get('siteEntry', []):
    print(f\"{site['siteUrl']}  |  Permission: {site['permissionLevel']}\")
"

The site URL format is either https://example.com/ (URL prefix) or sc-domain:example.com (domain property).

1. Search Performance Report

The core report: queries, pages, clicks, impressions, CTR, and average position.

API Endpoint

POST https://www.googleapis.com/webmasters/v3/sites/{siteUrl}/searchAnalytics/query

Note: The {siteUrl} must be URL-encoded (e.g., https%3A%2F%2Fexample.com%2F or sc-domain%3Aexample.com).

Top Queries

curl -s -X POST \
  "https://www.googleapis.com/webmasters/v3/sites/sc-domain%3Aexample.com/searchAnalytics/query" \
  -H "Authorization: Bearer ${GSC_ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "startDate": "2024-01-01",
    "endDate": "2024-03-31",
    "dimensions": ["query"],
    "rowLimit": 50,
    "startRow": 0
  }'

Top Pages

curl -s -X POST \
  "https://www.googleapis.com/webmasters/v3/sites/sc-domain%3Aexample.com/searchAnalytics/query" \
  -H "Authorization: Bearer ${GSC_ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "startDate": "2024-01-01",
    "endDate": "2024-03-31",
    "dimensions": ["page"],
    "rowLimit": 50
  }'

Query + Page Combination

curl -s -X POST \
  "https://www.googleapis.com/webmasters/v3/sites/sc-domain%3Aexample.com/searchAnalytics/query" \
  -H "Authorization: Bearer ${GSC_ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "startDate": "2024-01-01",
    "endDate": "2024-03-31",
    "dimensions": ["query", "page"],
    "rowLimit": 100,
    "dimensionFilterGroups": [{
      "filters": [{
        "dimension": "page",
        "operator": "contains",
        "expression": "/blog/"
      }]
    }]
  }'

Available Dimensions

Response Parsing

curl -s -X POST "..." | python3 -c "
import json, sys
data = json.load(sys.stdin)
print(f\"{'Query':<50} {'Clicks':>8} {'Impr':>8} {'CTR':>8} {'Pos':>6}\")
print('-' * 82)
for row in data.get('rows', []):
    keys = ' + '.join(row.get('keys', []))
    print(f\"{keys:<50} {row['clicks']:>8} {row['impressions']:>8} {row['ctr']*100:>7.1f}% {row['position']:>6.1f}\")
"

2. Search Performance by Date

Track daily trends for queries and pages.

curl -s -X POST \
  "https://www.googleapis.com/webmasters/v3/sites/sc-domain%3Aexample.com/searchAnalytics/query" \
  -H "Authorization: Bearer ${GSC_ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "startDate": "2024-01-01",
    "endDate": "2024-03-31",
    "dimensions": ["date"],
    "rowLimit": 1000
  }'

To track a specific query over time:

curl -s -X POST \
  "https://www.googleapis.com/webmasters/v3/sites/sc-domain%3Aexample.com/searchAnalytics/query" \
  -H "Authorization: Bearer ${GSC_ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "startDate": "2024-01-01",
    "endDate": "2024-03-31",
    "dimensions": ["date"],
    "dimensionFilterGroups": [{
      "filters": [{
        "dimension": "query",
        "operator": "equals",
        "expression": "your target keyword"
      }]
    }]
  }'

3. Index Coverage (URL Inspection API)

Check if a specific URL is indexed.

Endpoint

POST https://searchconsole.googleapis.com/v1/urlInspection/index:inspect

Example curl

curl -s -X POST \
  "https://searchconsole.googleapis.com/v1/urlInspection/index:inspect" \
  -H "Authorization: Bearer ${GSC_ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "inspectionUrl": "https://example.com/page-to-check",
    "siteUrl": "sc-domain:example.com"
  }'

Response Fields

4. Sitemaps

List and check sitemap status.

List Sitemaps

curl -s -H "Authorization: Bearer ${GSC_ACCESS_TOKEN}" \
  "https://www.googleapis.com/webmasters/v3/sites/sc-domain%3Aexample.com/sitemaps" \
  | python3 -c "
import json, sys
data = json.load(sys.stdin)
for sm in data.get('sitemap', []):
    print(f\"URL: {sm['path']}\")
    print(f\"  Type: {sm.get('type','')}  |  Submitted: {sm.get('lastSubmitted','')}\")
    print(f\"  URLs discovered: {sm.get('contents',[{}])[0].get('submitted','?')}  |  Indexed: {sm.get('contents',[{}])[0].get('indexed','?')}\")
    print()
"

Submit a Sitemap

curl -s -X PUT -H "Authorization: Bearer ${GSC_ACCESS_TOKEN}" \
  "https://www.googleapis.com/webmasters/v3/sites/sc-domain%3Aexample.com/sitemaps/https%3A%2F%2Fexample.com%2Fsitemap.xml"

5. Opportunity Identification

Use Search Console data to find SEO opportunities.

Low-Hanging Fruit: High Impressions, Low CTR

Queries with many impressions but low CTR suggest the title/description needs optimization.

# Pull queries, then filter for: impressions > 100 AND ctr < 0.03 AND position < 20
curl -s -X POST \
  "https://www.googleapis.com/webmasters/v3/sites/sc-domain%3Aexample.com/searchAnalytics/query" \
  -H "Authorization: Bearer ${GSC_ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "startDate": "2024-01-01",
    "endDate": "2024-03-31",
    "dimensions": ["query", "page"],
    "rowLimit": 1000
  }' | python3 -c "
import json, sys
data = json.load(sys.stdin)
print('== Low CTR Opportunities (High impressions, low CTR, good position) ==')
print(f\"{'Query':<40} {'Page':<40} {'Impr':>6} {'CTR':>7} {'Pos':>5}\")
for row in data.get('rows', []):
    if row['impressions'] > 100 and row['ctr'] < 0.03 and row['position'] < 20:
        print(f\"{row['keys'][0]:<40} {row['keys'][1][-40:]:<40} {row['impressions']:>6} {row['ctr']*100:>6.1f}% {row['position']:>5.1f}\")
"

Striking Distance: Position 5-20

Queries ranking on page 1-2 that could be pushed to top 5 with content optimization.

# Filter for position between 5 and 20 with decent impressions
curl -s -X POST "..." | python3 -c "
import json, sys
data = json.load(sys.stdin)
print('== Striking Distance Keywords (Position 5-20) ==')
opps = [r for r in data.get('rows',[]) if 5 <= r['position'] <= 20 and r['impressions'] > 50]
opps.sort(key=lambda x: x['impressions'], reverse=True)
for row in opps[:30]:
    print(f\"{row['keys'][0]:<50} Pos: {row['position']:>5.1f}  Impr: {row['impressions']:>6}  Clicks: {row['clicks']:>4}\")
"

Cannibalization Detection

Find queries where multiple pages compete for the same keyword.

# Pull query+page data, then group by query to find duplicates
curl -s -X POST "..." | python3 -c "
import json, sys
from collections import defaultdict
data = json.load(sys.stdin)
query_pages = defaultdict(list)
for row in data.get('rows', []):
    query_pages[row['keys'][0]].append({
        'page': row['keys'][1],
        'clicks': row['clicks'],
        'impressions': row['impressions'],
        'position': row['position']
    })
print('== Keyword Cannibalization (multiple pages for same query) ==')
for query, pages in sorted(query_pages.items(), key=lambda x: -sum(p['impressions'] for p in x[1])):
    if len(pages) > 1:
        total_impr = sum(p['impressions'] for p in pages)
        if total_impr > 100:
            print(f\"\nQuery: {query} ({total_impr} total impressions)\")
            for p in sorted(pages, key=lambda x: -x['impressions']):
                print(f\"  {p['page'][-60:]}  Pos: {p['position']:.1f}  Impr: {p['impressions']}  Clicks: {p['clicks']}\")
"

Workflow: Full Search Performance Audit

When asked for a complete GSC audit:

1. Overall Metrics: Total clicks, impressions, avg CTR, avg position for last 90 days vs previous 90 days
2. Top 30 Queries: By clicks, with CTR and position
3. Top 20 Pages: By clicks, with CTR and position
4. Device Breakdown: Desktop vs mobile performance
5. Low-Hanging Fruit: High impressions + low CTR opportunities
6. Striking Distance: Position 5-20 keywords with optimization potential
7. Cannibalization: Queries with multiple competing pages
8. Index Coverage: Spot-check important URLs
9. Sitemap Health: Verify sitemaps are submitted and indexed

Report Format

## Search Console Audit: {domain}
### Period: {date range}

### Summary
| Metric | Current | Previous | Change |
|--------|---------|----------|--------|
| Clicks | X | Y | +Z% |
| Impressions | X | Y | +Z% |
| Avg CTR | X% | Y% | +Z pp |
| Avg Position | X | Y | +Z |

### Top Queries
| Query | Clicks | Impressions | CTR | Position |
|-------|--------|-------------|-----|----------|
| ...   | ...    | ...         | ... | ...      |

### Optimization Opportunities

#### Title/Description Optimization (High Impressions, Low CTR)
1. "{query}" - {impressions} impressions, {ctr}% CTR, position {pos}
   - Page: {url}
   - Recommendation: ...

#### Content Optimization (Striking Distance)
1. "{query}" - position {pos}, {impressions} impressions
   - Action: Add {query} to H2, expand section on {topic}

#### Cannibalization Fixes
1. "{query}" appears on {n} pages
   - Consolidate to: {best_url}
   - Redirect/noindex: {other_urls}

Rate Limits

• Search Analytics API: 1,200 queries per minute
• URL Inspection API: 2,000 inspections per day per property
• Data freshness: Search data is typically 2-3 days behind

Common Errors