Step 1: Create Saved Search API

Note: list_size should match the length of the ID list you provide.

{
  "search_name": "Lukas_IDsTest_062525",
  "list_size": 10,
  "search_query": {
    "ids": [
      "335810662",
      "190167848",
      "191455659",
      "194066223",
      "187069145",
      "33726348",
      "35780942",
      "35809525",
      "34483531",
      "40671873"
    ]
  }
}

Example Response from Create Saved Search for List of IDs

{
  input: {
    search_name: 'Lukas_IDsTest_062525',
    list_size: 10,
    search_query: { ids: [Array], size: 10, ids_last_update: true }
  },
  data: {
    search: {
      searchId: '910d5054-8c88-4ee2-a139-284d4cxxxxxx',
      accountId: 96,
      xUserId: null,
      searchName: 'Lukas_IDsTest_062525',
      searchQuery: '{"ids":[335810662,190167848,191455659,194066223,187069145,33726348,35780942,35809525,34483531,40671873]}',
      lastReportDate: '2025-06-26T01:15:11.000Z',
      nextReportDate: '2025-06-27T01:15:11.000Z',
      createdAt: '2025-06-26T01:15:11.000Z',
      meta_data: {}
    },
    results: [
      [Object], [Object],
      [Object], [Object],
      [Object], [Object],
      [Object], [Object],
      [Object], [Object]
    ],
    summary: { size: 10, added: 10, deleted: 0, updated: 0, unchanged: 0 }
  },
  statusCode: 200,
  statusMessage: 'Success',
  credits: 0,
  live: true,
  requestExecutionTimeMS: '48ms'
}

Step 2: Get Daily Report and Make Updates Accordingly

{
  "searchId": "910d5054-8c88-4ee2-a139-284d4cxxxxxx"
}

Example Response from Retrieve Saved Search for List of IDs

{
  searchId: '910d5054-8c88-4ee2-a139-284d4cxxxxxx',
  accountId: 089080,
  xUserId: null,
  searchName: 'Lukas_IDsTest_062525',
  searchQuery: '{"ids":[335810662,190167848,191455659,194066223,187069145,33726348,35780942,35809525,34483531,40671873]}',
  lastReportDate: '2025-06-26T01:16:12.000Z',
  nextReportDate: '2025-06-27T01:16:12.000Z',
  createdAt: '2025-06-26T01:15:11.000Z',
  meta_data: {},
  results: [
    { id: 335810662, changeType: 'added', lastUpdateDate: '2025-06-11T00:00:00.000Z' },
    { id: 190167848, changeType: 'added', lastUpdateDate: '2025-06-18T00:00:00.000Z' },
    { id: 191455659, changeType: 'added', lastUpdateDate: '2025-06-18T00:00:00.000Z' },
    { id: 194066223, changeType: 'added', lastUpdateDate: '2025-06-18T00:00:00.000Z' },
    { id: 187069145, changeType: 'added', lastUpdateDate: '2025-06-11T00:00:00.000Z' },
    { id: 33726348, changeType: 'added', lastUpdateDate: '2025-06-19T00:00:00.000Z' },
    { id: 35780942, changeType: 'added', lastUpdateDate: '2025-06-18T00:00:00.000Z' },
    { id: 35809525, changeType: 'added', lastUpdateDate: '2025-06-18T00:00:00.000Z' },
    { id: 34483531, changeType: 'added', lastUpdateDate: '2025-06-11T00:00:00.000Z' },
    { id: 40671873, changeType: 'added', lastUpdateDate: '2025-06-18T00:00:00.000Z' }
  ],
  summary: { size: 10, added: 10, deleted: 0, updated: 0, unchanged: 0 }
}

What if I have more than 10,000 IDs to Track?

Since list_size max is 10,000, you'll need to break up your ids array input into multiple saved search queries
- Make sure the search_name you provide for each helps you serialize the results in your daily jobs
- e.g. "IDs_List_Seq_000", "IDs_List_Seq_001", "IDs_List_Seq_002", etc.
Once you hit enough properties (in the 10M+ range), you may get more benefit from tracking updates from all zip codes in easy size chunks to iterate through and compare to your larger index of IDs you are tracking
- One saved search per zip, and each day's Retrieve for each search_id can be prioritized where you have the most properties saved