Comment construire un scanner de citations GEO avec MCP

import os, requests, json
from datetime import datetime

API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}

DOMAIN = 'yourbrand.com'
KEYWORDS = [
    'best project management tool 2026',
    'project management software comparison',
    'how to choose project management app',
    'project management for small teams',
    'agile project management tools',
]

def scan_keyword(query):
    data = requests.post('https://api.scavio.dev/api/v1/search',
        headers=SH, json={'query': query, 'country_code': 'us'}).json()
    ai_overview = data.get('ai_overview', data.get('answer_box', {}))
    has_aio = bool(ai_overview)
    cited = False
    if has_aio:
        aio_text = json.dumps(ai_overview).lower()
        cited = DOMAIN.lower() in aio_text
    organic = data.get('organic_results', [])
    org_position = None
    for i, r in enumerate(organic):
        if DOMAIN in r.get('link', ''):
            org_position = i + 1
            break
    return {'query': query, 'has_aio': has_aio, 'cited': cited, 'organic_pos': org_position}

results = []
for kw in KEYWORDS:
    r = scan_keyword(kw)
    results.append(r)
    status = 'CITED' if r['cited'] else ('AIO present' if r['has_aio'] else 'No AIO')
    print(f'  [{status:12}] {kw[:50]:50} | org: #{r["organic_pos"] or "-"}')
print(f'\nCost: ${len(KEYWORDS) * 0.005:.3f}')

def geo_metrics(results):
    total = len(results)
    aio_count = sum(1 for r in results if r['has_aio'])
    cited_count = sum(1 for r in results if r['cited'])
    aio_rate = aio_count / total * 100 if total else 0
    citation_rate = cited_count / aio_count * 100 if aio_count else 0
    overall_rate = cited_count / total * 100 if total else 0
    organic_when_cited = [r['organic_pos'] for r in results if r['cited'] and r['organic_pos']]
    avg_org = sum(organic_when_cited) / len(organic_when_cited) if organic_when_cited else 0
    print(f'\n=== GEO Citation Report - {DOMAIN} ===')
    print(f'  Keywords scanned: {total}')
    print(f'  AI Overviews present: {aio_count} ({aio_rate:.0f}%)')
    print(f'  Your brand cited: {cited_count} ({overall_rate:.0f}% of all, {citation_rate:.0f}% of AIO)')
    if avg_org:
        print(f'  Avg organic position when cited: #{avg_org:.1f}')
    print(f'\n  Not cited but AIO present:')
    for r in results:
        if r['has_aio'] and not r['cited']:
            print(f'    - {r["query"][:50]}')
    return {'aio_rate': aio_rate, 'citation_rate': citation_rate}

geo_metrics(results)

def track_citations(results, history_file='geo_citations.json'):
    try:
        with open(history_file) as f:
            history = json.load(f)
    except FileNotFoundError:
        history = []
    today = {
        'date': datetime.now().strftime('%Y-%m-%d'),
        'domain': DOMAIN,
        'total': len(results),
        'aio_present': sum(1 for r in results if r['has_aio']),
        'cited': sum(1 for r in results if r['cited']),
        'details': results
    }
    history.append(today)
    with open(history_file, 'w') as f:
        json.dump(history, f, indent=2)
    print(f'\nSaved scan to {history_file}')
    if len(history) >= 2:
        prev = history[-2]
        delta_aio = today['aio_present'] - prev['aio_present']
        delta_cited = today['cited'] - prev['cited']
        print(f'  vs {prev["date"]}: AIO {delta_aio:+d}, Citations {delta_cited:+d}')
    print(f'  Run daily to build citation trend data')

track_citations(results)