Worldcat Search API

# Register at https://platform.worldcat.org/
# Obtain a WSKey (API key) for your application

# OAuth 2.0 client credentials flow
curl -X POST "https://oauth.oclc.org/token" \
  -u "$WSKEY_CLIENT_ID:$WSKEY_SECRET" \
  -d "grant_type=client_credentials&scope=wcapi"

https://www.worldcat.org/api/search/

# Keyword search
curl -H "Authorization: Bearer $TOKEN" \
  "https://www.worldcat.org/api/search?q=machine+learning&limit=25"

# Search by title
curl -H "Authorization: Bearer $TOKEN" \
  "https://www.worldcat.org/api/search?q=ti:attention+is+all+you+need"

# Search by author
curl -H "Authorization: Bearer $TOKEN" \
  "https://www.worldcat.org/api/search?q=au:hinton+geoffrey"

# Search by ISBN
curl -H "Authorization: Bearer $TOKEN" \
  "https://www.worldcat.org/api/search?q=bn:9780262035613"

# Combined filters
curl -H "Authorization: Bearer $TOKEN" \
  "https://www.worldcat.org/api/search?q=su:artificial+intelligence+AND+yr:2020-2026&itemType=book"

curl -H "Authorization: Bearer $TOKEN" \
  "https://www.worldcat.org/api/search/brief-bibs/{oclc_number}"

# Find libraries holding a specific item
curl -H "Authorization: Bearer $TOKEN" \
  "https://www.worldcat.org/api/search/brief-bibs/{oclc_number}/holdings?lat=42.36&lon=-71.06&distance=50"

{
  "numberOfRecords": 1250,
  "briefRecords": [
    {
      "oclcNumber": "1234567890",
      "title": "Deep Learning",
      "creator": "Ian Goodfellow; Yoshua Bengio; Aaron Courville",
      "date": "2016",
      "publisher": "MIT Press",
      "language": "eng",
      "generalFormat": "Book",
      "specificFormat": "PrintBook",
      "isbns": ["9780262035613"],
      "catalogingInfo": {
        "catalogingAgency": "DLC"
      },
      "totalHoldingCount": 3542
    }
  ]
}

import os
import requests

CLIENT_ID = os.environ["OCLC_WSKEY_ID"]
CLIENT_SECRET = os.environ["OCLC_WSKEY_SECRET"]
BASE_URL = "https://www.worldcat.org/api/search"


def get_token() -> str:
    """Obtain OAuth token from OCLC."""
    resp = requests.post(
        "https://oauth.oclc.org/token",
        auth=(CLIENT_ID, CLIENT_SECRET),
        data={"grant_type": "client_credentials", "scope": "wcapi"},
    )
    resp.raise_for_status()
    return resp.json()["access_token"]


def search_worldcat(query: str, limit: int = 25,
                    item_type: str = None) -> list:
    """Search WorldCat bibliographic records."""
    token = get_token()
    params = {"q": query, "limit": limit}
    if item_type:
        params["itemType"] = item_type

    resp = requests.get(
        BASE_URL,
        headers={"Authorization": f"Bearer {token}"},
        params=params,
    )
    resp.raise_for_status()
    data = resp.json()

    results = []
    for rec in data.get("briefRecords", []):
        results.append({
            "oclc": rec.get("oclcNumber"),
            "title": rec.get("title"),
            "creator": rec.get("creator"),
            "date": rec.get("date"),
            "publisher": rec.get("publisher"),
            "format": rec.get("generalFormat"),
            "holdings": rec.get("totalHoldingCount", 0),
            "isbns": rec.get("isbns", []),
        })
    return results


def find_nearby_holdings(oclc_number: str,
                         lat: float, lon: float,
                         distance_km: int = 50) -> list:
    """Find libraries near a location that hold a specific item."""
    token = get_token()
    resp = requests.get(
        f"{BASE_URL}/brief-bibs/{oclc_number}/holdings",
        headers={"Authorization": f"Bearer {token}"},
        params={"lat": lat, "lon": lon, "distance": distance_km},
    )
    resp.raise_for_status()
    return resp.json().get("briefRecords", [])


# Example: find widely-held ML textbooks
books = search_worldcat("su:machine learning AND yr:2020-2026",
                        item_type="book", limit=10)
for b in books:
    print(f"[{b['date']}] {b['title']} — {b['publisher']} "
          f"(held by {b['holdings']} libraries)")