Combine Surfer SEO with a search API to pull real-time SERP data alongside Surfer's content recommendations. Surfer SEO provides content structure and keyword density guidance, but it does not show you the live SERP landscape as you write. By querying the search API for your target keyword, you can see current featured snippets, People Also Ask questions, and competitor titles in real time. This lets you align your content with both Surfer's NLP-based recommendations and the actual search results page.
Prerequisites
- Python 3.8+ installed
- requests library installed
- A Scavio API key from scavio.dev
- A Surfer SEO account (Essential $99/mo or higher)
Walkthrough
Step 1: Query SERP for target keyword
Pull the current Google SERP for the keyword you are optimizing in Surfer SEO.
Python
import os, requests, json
API_KEY = os.environ['SCAVIO_API_KEY']
def get_serp(keyword: str) -> dict:
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': API_KEY},
json={'platform': 'google', 'query': keyword}, timeout=15)
return resp.json()
serp = get_serp('best project management tools 2026')
print(f"Organic results: {len(serp.get('organic_results', []))}")
print(f"People Also Ask: {len(serp.get('people_also_ask', []))}")Step 2: Extract competitor content signals
Analyze the top-ranking pages to understand their title patterns, word count signals, and content angles.
Python
def analyze_competitors(serp: dict) -> list:
competitors = []
for r in serp.get('organic_results', [])[:10]:
competitors.append({
'position': r.get('position', 0),
'title': r.get('title', ''),
'url': r.get('link', ''),
'snippet_length': len(r.get('snippet', '')),
'has_date': any(str(y) in r.get('title', '') for y in [2025, 2026]),
'has_number': any(c.isdigit() for c in r.get('title', '')),
})
return competitors
comps = analyze_competitors(serp)
for c in comps[:5]:
print(f"#{c['position']}: {c['title'][:55]} (date={c['has_date']})")Step 3: Capture People Also Ask
Pull PAA questions to incorporate into your Surfer content as H2 or H3 subheadings.
Python
def get_paa(serp: dict) -> list:
paa = serp.get('people_also_ask', [])
questions = []
for item in paa:
if isinstance(item, dict):
questions.append(item.get('question', ''))
elif isinstance(item, str):
questions.append(item)
return questions
questions = get_paa(serp)
print('People Also Ask questions to address in your content:')
for q in questions:
print(f' - {q}')Step 4: Extract featured snippet format
Determine the current featured snippet format so you can structure your content to compete.
Python
def analyze_snippet(serp: dict) -> dict:
snippet = serp.get('featured_snippet', serp.get('answer_box', {}))
if not snippet or not isinstance(snippet, dict):
return {'type': 'none', 'exists': False}
snippet_text = snippet.get('snippet', snippet.get('answer', ''))
has_list = '<li>' in str(snippet) or isinstance(snippet.get('list'), list)
has_table = '<table>' in str(snippet) or isinstance(snippet.get('table'), list)
return {
'exists': True,
'type': 'list' if has_list else 'table' if has_table else 'paragraph',
'text_length': len(str(snippet_text)),
'source': snippet.get('link', snippet.get('source', '')),
}
fs = analyze_snippet(serp)
print(f"Featured snippet: {fs['type']} ({fs.get('text_length', 0)} chars)")Step 5: Generate content brief
Combine SERP insights with Surfer SEO recommendations into a single content brief.
Python
def generate_brief(keyword: str) -> dict:
serp = get_serp(keyword)
comps = analyze_competitors(serp)
paa = get_paa(serp)
snippet = analyze_snippet(serp)
titles_with_dates = sum(1 for c in comps if c['has_date'])
brief = {
'keyword': keyword,
'competitor_count': len(comps),
'titles_with_year': titles_with_dates,
'paa_questions': paa,
'featured_snippet': snippet,
'top_3_titles': [c['title'] for c in comps[:3]],
'recommendations': [],
}
if titles_with_dates > 3:
brief['recommendations'].append('Include 2026 in your title')
if snippet['exists']:
brief['recommendations'].append(f"Target a {snippet['type']} featured snippet")
if paa:
brief['recommendations'].append(f'Address {len(paa)} PAA questions as subheadings')
return brief
brief = generate_brief('best project management tools 2026')
for rec in brief['recommendations']:
print(f' > {rec}')Python Example
Python
import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
def serp_brief(keyword):
data = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'platform': 'google', 'query': keyword}).json()
titles = [r['title'] for r in data.get('organic_results', [])[:5]]
paa = [q.get('question', q) if isinstance(q, dict) else q for q in data.get('people_also_ask', [])]
return {'titles': titles, 'paa': paa}
print(serp_brief('best project management tools 2026'))JavaScript Example
JavaScript
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
async function serpBrief(keyword) {
const r = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: H,
body: JSON.stringify({platform: 'google', query: keyword})
});
const data = await r.json();
return {
titles: (data.organic_results || []).slice(0, 5).map(r => r.title),
paa: (data.people_also_ask || []).map(q => q.question || q)
};
}
serpBrief('best project management tools 2026').then(console.log);Expected Output
JSON
A content brief combining Surfer SEO recommendations with live SERP data including competitor titles, PAA questions, and featured snippet analysis for better content optimization.