Deep Query: Aggregate Synced Data for AI Agents

Deep Query use cases. Query any system of record
like a database.

Deep Query filters, counts, sums and groups synced records inside StackOne, so your agent gets totals, breakdowns and trends instead of the records behind them. Pick an example to compare it with the provider's own API.

Which 10 companies have the most open pipeline value?

Pipedrive deals 2,400 synced deals1,500 open10 organization totals

Kestrel Energy $635,000 · 30 deals Meridian Bank $560,000 · 20 deals Tidewater Shipping $465,000 · 60 deals Alder Pharma $440,000 · 30 deals Harbor Analytics $360,000 · 90 deals

pipedrive_list_deals_s1query

{
  "filter": {"term": {"status": "open"}},
  "aggs": {
    "by_organization": {
      "terms": {"field": "org_id", "size": 10, "order": {"total": "desc"}},
      "aggs": {"total": {"sum": {"field": "value"}}}
    }
  }
}

pipedrive_list_organizations_s1query

{
  "filter": {
    "terms": {"id": [7002, 7001, 7004, 7020, 7003, 7033, 7019, 7009, 7005, 7015]}
  },
  "source": ["name", "id"]
}

Then the agent sums value by org_id across the open deals itself, keeps the top 10 and asks for their names.

Kestrel Energy has the most open pipeline at $635,000, then Meridian Bank at $560,000.

One of 240 synced records

{
  "id": 51000,
  "title": "Cedar Logistics renewal",
  "value": 2500,
  "currency": "USD",
  "status": "open",
  "org_id": 7022,
  "owner_id": 31,
  "stage_id": 3,
  "pipeline_id": 1,
  "probability": 35,
  "add_time": "2026-01-31T09:00:00Z",
  "expected_close_date": "2027-01-02"
}

One of 40 synced records

{
  "id": 7001,
  "name": "Meridian Bank",
  "owner_id": 31,
  "address": "100 Main Street, New York, NY",
  "add_time": "2024-01-15T10:00:00Z"
}

pipedrive_list_deals_s1queryFilter open deals, sum value for the top 10 organizations

{
  "filter": {
    "term": {
      "status": "open"
    }
  },
  "aggs": {
    "by_organization": {
      "terms": {
        "field": "org_id",
        "size": 10,
        "order": {
          "total": "desc"
        }
      },
      "aggs": {
        "total": {
          "sum": {
            "field": "value"
          }
        }
      }
    }
  }
}

pipedrive_list_organizations_s1queryLook up the 10 organizations by id

{
  "filter": {
    "terms": {
      "id": [
        7002,
        7001,
        7004,
        7020,
        7003,
        7033,
        7019,
        7009,
        7005,
        7015
      ]
    }
  },
  "source": [
    "name",
    "id"
  ]
}

All results

Kestrel Energy	$635,000 · 30 deals
Meridian Bank	$560,000 · 20 deals
Tidewater Shipping	$465,000 · 60 deals
Alder Pharma	$440,000 · 30 deals
Harbor Analytics	$360,000 · 90 deals
Nimbus Cloud	$345,000 · 50 deals
Zephyr Airlines	$320,000 · 40 deals
Redwood Capital	$315,000 · 20 deals
Pinnacle Insurance	$290,000 · 40 deals
Upland Software	$277,500 · 80 deals

Returned by pipedrive_list_deals_s1query

[
  {
    "key": "7002",
    "doc_count": 30,
    "total": {
      "value": 635000
    }
  },
  {
    "key": "7001",
    "doc_count": 20,
    "total": {
      "value": 560000
    }
  },
  {
    "key": "7004",
    "doc_count": 60,
    "total": {
      "value": 465000
    }
  },
  {
    "key": "7020",
    "doc_count": 30,
    "total": {
      "value": 440000
    }
  },
  {
    "key": "7003",
    "doc_count": 90,
    "total": {
      "value": 360000
    }
  },
  {
    "key": "7033",
    "doc_count": 50,
    "total": {
      "value": 345000
    }
  },
  {
    "key": "7019",
    "doc_count": 40,
    "total": {
      "value": 320000
    }
  },
  {
    "key": "7009",
    "doc_count": 20,
    "total": {
      "value": 315000
    }
  },
  {
    "key": "7005",
    "doc_count": 40,
    "total": {
      "value": 290000
    }
  },
  {
    "key": "7015",
    "doc_count": 80,
    "total": {
      "value": 277500
    }
  }
]

Returned by pipedrive_list_organizations_s1query

[
  {
    "name": "Meridian Bank",
    "id": 7001
  },
  {
    "name": "Kestrel Energy",
    "id": 7002
  },
  {
    "name": "Harbor Analytics",
    "id": 7003
  },
  {
    "name": "Tidewater Shipping",
    "id": 7004
  },
  {
    "name": "Pinnacle Insurance",
    "id": 7005
  },
  {
    "name": "Redwood Capital",
    "id": 7009
  },
  {
    "name": "Upland Software",
    "id": 7015
  },
  {
    "name": "Zephyr Airlines",
    "id": 7019
  },
  {
    "name": "Alder Pharma",
    "id": 7020
  },
  {
    "name": "Nimbus Cloud",
    "id": 7033
  }
]

Deep Query

5,056 tokens

Live API

498.8K tokens

98.9%fewer tokens

$200$2.02per 100 runs

Model

Without Deep Query, the agent asks the provider API for open deals, up to 500 per call, one page after another, and does the counting, grouping or summing itself. Then the agent asks the provider API for the 10 organizations by id. A call takes about 0.3 seconds plus time for what it returns, the agent spends about 3 seconds on each turn, and the model reads everything it gets at about 6,500 tokens a second. Deep Query answers a query in about 0.2 seconds, and the agent sends its 2 queries one after the other. Records are measured with the o200k_base tokenizer and priced at the model's public input rate. Both sides include about 2,600 tokens for the prompt and the query tool's definition, and the Deep Query side about 2,000 more for extra field lookups, retried queries and the agent's output, plus about 160 for each extra query.

~2 min~7 sper run

Which 10 suppliers did we spend the most with this quarter?

Xero 1,150 invoices + 2,200 bank transactions600 bills + 1,700 payments this quarter49 supplier totals

Westgate Property $555,000 · 30 bills + 0 payments Tandem Consulting $470,000 · 40 bills + 0 payments Cloudline Hosting $233,770 · 0 bills + 300 payments Vertex Software $154,700 · 20 bills + 120 payments Metro Travel $151,080 · 0 bills + 400 payments

xero_list_invoices_s1query

{
  "filter": {
    "bool": {
      "must": [
        {"term": {"Type": "ACCPAY"}},
        {"range": {"Date": {"gte": "2026-07-01"}}}
      ]
    }
  },
  "aggs": {
    "by_supplier": {
      "terms": {"field": "Contact.Name", "size": 50, "order": {"total": "desc"}},
      "aggs": {"total": {"sum": {"field": "Total"}}}
    }
  }
}

xero_list_bank_transactions_s1query

{
  "filter": {
    "bool": {
      "must": [
        {"term": {"Type": "SPEND"}},
        {"range": {"Date": {"gte": "2026-07-01"}}}
      ]
    }
  },
  "aggs": {
    "by_supplier": {
      "terms": {"field": "Contact.Name", "size": 50, "order": {"total": "desc"}},
      "aggs": {"total": {"sum": {"field": "Total"}}}
    }
  }
}

Then the agent adds up bills and payments by supplier itself and keeps the top 10.

Westgate Property was paid the most this quarter at $555,000, then Tandem Consulting at $470,000.

One of 115 synced records

{
  "InvoiceID": "972d5c56-0310-4310-8170-000000000310",
  "InvoiceNumber": "INV-02200",
  "Type": "ACCREC",
  "Status": "PAID",
  "Contact.Name": "Kestrel Energy",
  "Date": "2026-07-03",
  "DueDate": "2026-07-28",
  "Total": 3500,
  "CurrencyCode": "USD"
}

One of 220 synced records

{
  "BankTransactionID": "3b07d244-0900-4900-8300-000000000900",
  "Type": "SPEND",
  "Status": "AUTHORISED",
  "Contact.Name": "Ridge Telecom",
  "Date": "2026-07-04",
  "Total": 55,
  "BankAccount.Name": "Operating account",
  "Reference": "Monthly plan",
  "CurrencyCode": "USD"
}

xero_list_invoices_s1queryFilter this quarter's bills, sum by supplier

{
  "filter": {
    "bool": {
      "must": [
        {
          "term": {
            "Type": "ACCPAY"
          }
        },
        {
          "range": {
            "Date": {
              "gte": "2026-07-01"
            }
          }
        }
      ]
    }
  },
  "aggs": {
    "by_supplier": {
      "terms": {
        "field": "Contact.Name",
        "size": 50,
        "order": {
          "total": "desc"
        }
      },
      "aggs": {
        "total": {
          "sum": {
            "field": "Total"
          }
        }
      }
    }
  }
}

xero_list_bank_transactions_s1queryFilter this quarter's spend, sum by supplier

{
  "filter": {
    "bool": {
      "must": [
        {
          "term": {
            "Type": "SPEND"
          }
        },
        {
          "range": {
            "Date": {
              "gte": "2026-07-01"
            }
          }
        }
      ]
    }
  },
  "aggs": {
    "by_supplier": {
      "terms": {
        "field": "Contact.Name",
        "size": 50,
        "order": {
          "total": "desc"
        }
      },
      "aggs": {
        "total": {
          "sum": {
            "field": "Total"
          }
        }
      }
    }
  }
}

Combined supplier totals

Westgate Property	$555,000 · 30 bills + 0 payments
Tandem Consulting	$470,000 · 40 bills + 0 payments
Cloudline Hosting	$233,770 · 0 bills + 300 payments
Vertex Software	$154,700 · 20 bills + 120 payments
Metro Travel	$151,080 · 0 bills + 400 payments
Brightpath Recruiting	$145,000 · 20 bills + 0 payments
Harbor Legal LLP	$134,000 · 30 bills + 0 payments
Upstream Marketing	$131,350 · 30 bills + 40 payments
Quantum Payroll Services	$126,000 · 30 bills + 0 payments
Ironwood Facilities	$63,000 · 30 bills + 0 payments

Returned by xero_list_invoices_s1query

[
  {
    "key": "Westgate Property",
    "doc_count": 30,
    "total": {
      "value": 555000
    }
  },
  {
    "key": "Tandem Consulting",
    "doc_count": 40,
    "total": {
      "value": 470000
    }
  },
  {
    "key": "Brightpath Recruiting",
    "doc_count": 20,
    "total": {
      "value": 145000
    }
  },
  {
    "key": "Harbor Legal LLP",
    "doc_count": 30,
    "total": {
      "value": 134000
    }
  },
  {
    "key": "Quantum Payroll Services",
    "doc_count": 30,
    "total": {
      "value": 126000
    }
  },
  {
    "key": "Vertex Software",
    "doc_count": 20,
    "total": {
      "value": 108000
    }
  },
  {
    "key": "Upstream Marketing",
    "doc_count": 30,
    "total": {
      "value": 95000
    }
  },
  {
    "key": "Ironwood Facilities",
    "doc_count": 30,
    "total": {
      "value": 63000
    }
  },
  {
    "key": "Keystone Insurance",
    "doc_count": 10,
    "total": {
      "value": 59000
    }
  },
  {
    "key": "Canal Print",
    "doc_count": 50,
    "total": {
      "value": 21000
    }
  },
  {
    "key": "Zinc Hardware",
    "doc_count": 40,
    "total": {
      "value": 19000
    }
  },
  {
    "key": "Iris Translation",
    "doc_count": 30,
    "total": {
      "value": 17500
    }
  },
  {
    "key": "Loom Furniture",
    "doc_count": 30,
    "total": {
      "value": 14000
    }
  },
  {
    "key": "Fern Florists",
    "doc_count": 20,
    "total": {
      "value": 13500
    }
  },
  {
    "key": "Sage Accounting",
    "doc_count": 20,
    "total": {
      "value": 11500
    }
  },
  {
    "key": "Maple Waste",
    "doc_count": 20,
    "total": {
      "value": 11000
    }
  },
  {
    "key": "Vale Maintenance",
    "doc_count": 30,
    "total": {
      "value": 10500
    }
  },
  {
    "key": "Unity Legal",
    "doc_count": 20,
    "total": {
      "value": 7500
    }
  },
  {
    "key": "Wren Design",
    "doc_count": 10,
    "total": {
      "value": 7000
    }
  },
  {
    "key": "Oak Plumbing",
    "doc_count": 10,
    "total": {
      "value": 6000
    }
  },
  {
    "key": "Anchor Signs",
    "doc_count": 10,
    "total": {
      "value": 5500
    }
  },
  {
    "key": "Tide Water Co",
    "doc_count": 10,
    "total": {
      "value": 5000
    }
  },
  {
    "key": "Grove IT Support",
    "doc_count": 20,
    "total": {
      "value": 4500
    }
  },
  {
    "key": "Ridge Telecom",
    "doc_count": 10,
    "total": {
      "value": 4000
    }
  },
  {
    "key": "Quartz Labs",
    "doc_count": 10,
    "total": {
      "value": 3500
    }
  },
  {
    "key": "Pier Parking",
    "doc_count": 10,
    "total": {
      "value": 3000
    }
  },
  {
    "key": "Nova Training",
    "doc_count": 10,
    "total": {
      "value": 2500
    }
  }
]

Returned by xero_list_bank_transactions_s1query

[
  {
    "key": "Cloudline Hosting",
    "doc_count": 300,
    "total": {
      "value": 233770
    }
  },
  {
    "key": "Metro Travel",
    "doc_count": 400,
    "total": {
      "value": 151080
    }
  },
  {
    "key": "Vertex Software",
    "doc_count": 120,
    "total": {
      "value": 46700
    }
  },
  {
    "key": "Pixel Studio",
    "doc_count": 60,
    "total": {
      "value": 42290
    }
  },
  {
    "key": "Upstream Marketing",
    "doc_count": 40,
    "total": {
      "value": 36350
    }
  },
  {
    "key": "Summit Office Supplies",
    "doc_count": 250,
    "total": {
      "value": 29100
    }
  },
  {
    "key": "Silverleaf Catering",
    "doc_count": 180,
    "total": {
      "value": 27630
    }
  },
  {
    "key": "Redline Couriers",
    "doc_count": 150,
    "total": {
      "value": 11660
    }
  },
  {
    "key": "Yarrow Health",
    "doc_count": 20,
    "total": {
      "value": 5600
    }
  },
  {
    "key": "Ember Electric",
    "doc_count": 20,
    "total": {
      "value": 4100
    }
  },
  {
    "key": "Nova Training",
    "doc_count": 20,
    "total": {
      "value": 4100
    }
  },
  {
    "key": "Vale Maintenance",
    "doc_count": 20,
    "total": {
      "value": 4100
    }
  },
  {
    "key": "Ridge Telecom",
    "doc_count": 20,
    "total": {
      "value": 3350
    }
  },
  {
    "key": "Kite Events",
    "doc_count": 10,
    "total": {
      "value": 3250
    }
  },
  {
    "key": "Unity Legal",
    "doc_count": 20,
    "total": {
      "value": 3050
    }
  },
  {
    "key": "Grove IT Support",
    "doc_count": 10,
    "total": {
      "value": 2500
    }
  },
  {
    "key": "Loom Furniture",
    "doc_count": 10,
    "total": {
      "value": 2500
    }
  },
  {
    "key": "Quartz Labs",
    "doc_count": 10,
    "total": {
      "value": 2050
    }
  },
  {
    "key": "Canal Print",
    "doc_count": 10,
    "total": {
      "value": 1300
    }
  },
  {
    "key": "Sage Accounting",
    "doc_count": 10,
    "total": {
      "value": 1300
    }
  },
  {
    "key": "Haven Security",
    "doc_count": 10,
    "total": {
      "value": 550
    }
  },
  {
    "key": "Oak Plumbing",
    "doc_count": 10,
    "total": {
      "value": 550
    }
  }
]

Deep Query

5,684 tokens

Live API

619K tokens

99%fewer tokens

$248$2.27per 100 runs

Model

Without Deep Query, the agent asks the provider API for this quarter's bills, up to 1,000 per call, one page after another, and does the counting, grouping or summing itself. In the same turns, the agent asks the provider API for this quarter's spend transactions, up to 1,000 per call, one page after another, and does the counting, grouping or summing itself. A call takes about 0.3 seconds plus time for what it returns, the agent spends about 3 seconds on each turn, and the model reads everything it gets at about 6,500 tokens a second. Deep Query answers a query in about 0.2 seconds, and the agent sends its 2 queries in one turn. Records are measured with the o200k_base tokenizer and priced at the model's public input rate. Both sides include about 2,600 tokens for the prompt and the query tool's definition, and the Deep Query side about 2,000 more for extra field lookups, retried queries and the agent's output, plus about 160 for each extra query.

~2 min~4 sper run

Which 10 customers opened the most refund tickets this quarter?

Zendesk tickets 9,600 synced tickets1,740 refund tickets this quarter10 organization counts

Northwind Traders 390 Contoso 150 Tailspin Toys 150 Fourth Coffee 120 Proseware 120

{
  "filter": {
    "bool": {
      "must": [
        {"match": {"description": "refund"}},
        {"range": {"createdAt": {"gte": "2026-07-01"}}}
      ]
    }
  },
  "aggs": {"by_organization": {"terms": {"field": "organization", "size": 10}}}
}

Then the agent compares the 300 counts and keeps the top 10.

Northwind Traders opened the most refund tickets this quarter (390), then Contoso (150).

One of 320 synced records

{
  "id": 480000,
  "subject": "The customer is asking for a",
  "description": "The customer is asking for a refund on their last order. Export to CSV fails with a timeout.",
  "status": "open",
  "group": "Shipping",
  "priority": "high",
  "requester": "Jordan Novak",
  "createdAt": "2026-09-01",
  "organization": "Consolidated Messenger"
}

zendesk_list_tickets_s1querySearch descriptions for refund this quarter, count the top 10 organizations

{
  "filter": {
    "bool": {
      "must": [
        {
          "match": {
            "description": "refund"
          }
        },
        {
          "range": {
            "createdAt": {
              "gte": "2026-07-01"
            }
          }
        }
      ]
    }
  },
  "aggs": {
    "by_organization": {
      "terms": {
        "field": "organization",
        "size": 10
      }
    }
  }
}

All results

Northwind Traders	390
Contoso	150
Tailspin Toys	150
Fourth Coffee	120
Proseware	120
Adventure Works	90
Alpine Ski House	60
Bellows College	60
Blue Yonder Airlines	60
Consolidated Messenger	60

Returned by zendesk_list_tickets_s1query

[
  {
    "key": "Northwind Traders",
    "doc_count": 390
  },
  {
    "key": "Contoso",
    "doc_count": 150
  },
  {
    "key": "Tailspin Toys",
    "doc_count": 150
  },
  {
    "key": "Fourth Coffee",
    "doc_count": 120
  },
  {
    "key": "Proseware",
    "doc_count": 120
  },
  {
    "key": "Adventure Works",
    "doc_count": 90
  },
  {
    "key": "Alpine Ski House",
    "doc_count": 60
  },
  {
    "key": "Bellows College",
    "doc_count": 60
  },
  {
    "key": "Blue Yonder Airlines",
    "doc_count": 60
  },
  {
    "key": "Consolidated Messenger",
    "doc_count": 60
  }
]

Deep Query

4,712 tokens

Live API

87,680 tokens

94.6%fewer tokens

$35.07$1.88per 100 runs

Model

Without Deep Query, the agent lists the 300 customer organizations and asks the provider API for one count each, up to 10 calls per turn. A call takes about 0.3 seconds plus time for what it returns, the agent spends about 3 seconds on each turn, and the model reads everything it gets at about 6,500 tokens a second. Deep Query answers a query in about 0.2 seconds, and its run is one query and one turn. Records are measured with the o200k_base tokenizer and priced at the model's public input rate. Both sides include about 2,600 tokens for the prompt and the query tool's definition, and the Deep Query side about 2,000 more for extra field lookups, retried queries and the agent's output.

~2 min~4 sper run

Records

Status

Topic

Top 10 by count

Zendesk tickets 9,600 synced tickets2,220 tickets mentioning a refund10 counts by organization

Northwind Traders 480 Contoso 210 Litware 180 Tailspin Toys 180

{
  "filter": {"match": {"description": "refund"}},
  "aggs": {"by_organization": {"terms": {"field": "organization"}}}
}

Then the agent compares the 300 counts and keeps the top 10.

2,220 tickets, the most for Northwind Traders (480).

One of 9,600 synced records

{
  "id": 480000,
  "subject": "The customer is asking for a",
  "description": "The customer is asking for a refund on their last order. Export to CSV fails with a timeout.",
  "status": "open",
  "group": "Shipping",
  "priority": "high",
  "requester": "Jordan Novak",
  "createdAt": "2026-09-01",
  "organization": "Consolidated Messenger"
}

zendesk_list_tickets_s1queryBuilt from your choices

{
  "filter": {
    "match": {
      "description": "refund"
    }
  },
  "aggs": {
    "by_organization": {
      "terms": {
        "field": "organization"
      }
    }
  }
}

Returned by zendesk_list_tickets_s1query

[
  {
    "key": "Northwind Traders",
    "doc_count": 480
  },
  {
    "key": "Contoso",
    "doc_count": 210
  },
  {
    "key": "Litware",
    "doc_count": 180
  },
  {
    "key": "Tailspin Toys",
    "doc_count": 180
  },
  {
    "key": "Fourth Coffee",
    "doc_count": 150
  },
  {
    "key": "Proseware",
    "doc_count": 120
  },
  {
    "key": "Adventure Works",
    "doc_count": 90
  },
  {
    "key": "Bellows College",
    "doc_count": 90
  },
  {
    "key": "Consolidated Messenger",
    "doc_count": 90
  },
  {
    "key": "Humongous Insurance",
    "doc_count": 90
  }
]

Deep Query

4,710 tokens

Live API

87,680 tokens

94.6%fewer tokens

$35.07$1.88per 100 runs

Model

Without Deep Query, the agent lists the 300 customer organizations and asks the provider API for one count each, up to 10 calls per turn. A call takes about 0.3 seconds plus time for what it returns, the agent spends about 3 seconds on each turn, and the model reads everything it gets at about 6,500 tokens a second. Deep Query answers a query in about 0.2 seconds, and its run is one query and one turn. Records are measured with the o200k_base tokenizer and priced at the model's public input rate. Both sides include about 2,600 tokens for the prompt and the query tool's definition, and the Deep Query side about 2,000 more for extra field lookups, retried queries and the agent's output.

~2 min~4 sper run

These examples run on fictional records. Their token counts, costs and times are modelled from the records each call returns. For measured results, see the Deep Query benchmark.

Deep Query benchmark. Half the tokens
on the same tasks.

On the same 1,345 benchmark tasks, agents with Deep Query used half the tokens. They also completed more of them, because fewer tool responses overflowed the agent's context.

Tokens used

All 1,345 tasks

Lower is better

50% fewer

Tasks completed

Share that met their goal

Higher is better

+28 points

Truncated responses

Share of tasks over 50 KB

Lower is better

−24 points

Method. In a StackOne benchmark in September 2026, the same agent ran 1,345 tasks on seven connectors twice, with and without Deep Query. Results vary by agent and model.

Faster tool responses. Read synced data
without waiting on the source API.

Deep Query reads records already stored in StackOne, so your agent gets an answer even while a provider's API is slow or rate-limited.

Live lookup0.2 to 12.7 s

Agent Provider APIAnswer

Deep Query About 0.2 s

Agent Synced recordsAnswer

A live lookup waits 0.2 to 12.7 seconds for the provider API to answer. Deep Query reads the synced records in about 0.2 seconds.

Hear it on a voice call

Every second an agent waits on a tool is a second of silence for the caller. Press play to hear the same question answered both ways: fetching 500 employees live takes 17 seconds at the median in production.

CallerHey, how many people do we have in the Berlin office right now?

Without Deep Query sapsuccessfactors_list_users

17.2 s of waiting before the answer

With Deep Query sapsuccessfactors_list_users_s1query

0.11 s before the answer

Wait times are production medians: 17 s to fetch 500 employees live, 0.11 s from synced records. The voices are a recreation, so turn your sound on.

Configure Data Sync. Set how often Data Sync
refreshes your records.

Choose which actions to sync and set a schedule for each one. Deep Query reads those stored records, so the schedule sets how fresh its results are.

Follow the Data Sync setup guide

01Pick an actionOpen a Connector Profile and find a list action that supports sync.

PipedriveProfile · Sales agent

ActionType

List DealsRead

Get DealRead

List OrganizationsRead

Update DealWrite

02Enable Data SyncSelect the sync icon and switch Data Sync on for that action.

Data Sync· List DealsOptimize

Setup

Enable Data Sync

Preferences

Sync every 1 hour

Full re-sync 1 day

Save Changes

03Set the scheduleChoose how often new and changed records sync, and how often a full re-sync runs.

Data Sync· List DealsOptimize

Setup

Enable Data Sync

Preferences

Sync every 1 hour

Full re-sync 1 day

Save Changes

04SaveSave the profile to start syncing.

Data Sync· List DealsOptimize

Setup

Enable Data Sync

Preferences

Sync every 1 hour

Full re-sync 1 day

Save Changes Data Sync saved

05StackOne syncsStackOne pages through the provider API on your schedule and keeps the records current.

Pipedrive API

Synced records2,400

Full sync2,400 records
Incremental38 changed
Incremental12 changed

06Your agent queriesThe action's query tool is ready over MCP to filter, search and aggregate.

How many open deals are in each pipeline stage?

pipedrive_list_deals_s1query1 query

filter: status open
aggs:   terms stage_id

1,500 open deals across 5 stages, 380 of them in stage 3.

Sync any list action, including your own.

An action declares Data Sync in its connector's YAML before anyone can turn it on. StackOne's connectors declare it for common records. In your own connector, you choose. Edit the block to see how it changes each sync run and the tool your agent gets.

Build your own connector Read the YAML reference

hrportal.workers.s1.partial.yaml

- actionId: list_workers
  dataSync:
    allowed: true
    indexField: id
    pagination:
      type: cursor
      response:
        dataKey: data
        nextKey: next
      iterator:
        key: next
        in: query
    incremental:
      type: time_filter
      iterator:
        key: updated_after
        in: query
      format: iso8601
      response:
        updatedAtPath: updated_at

Valid. The action offers Data Sync.

In the Connector Profile1

List WorkersSynced hourly · changes only Read

Data Sync can't be turned on while allowed is false.

Sync run2

237workers syncedchanged since the last run

GET/workers?updated_after=2026-09-24T09:00:00Z100 · next=c_8f2a
GET/workers?next=c_8f2a&updated_after=2026-09-24T09:00:00Z100 · next=c_90bd
GET/workers?next=c_90bd&updated_after=2026-09-24T09:00:00Z37 · no next
Done in 3 requests. Unchanged workers stay as they were.

What your agent gets3

hrportal_list_workers_s1query

Filter, search, sort and count your synced workers, in about 0.2 seconds.

departmentstatuscountryjobTitlestartDateprobationEndDate

filtermatchsortaggssource

No query tool. The agent can only page through your system's live API.

Deep Query syntax. Filter, search and count
on any synced field.

Query tools take OpenSearch-style queries. Your agent looks up each synced field and its type first, then filters, searches and aggregates on it.

Filter

termequals
termsany of
rangegt · gte · lt · lte
existshas a value
wildcardEng*
booland · or · not

Search text

matchany of the words
match_phrasethe exact phrase
search"phrases" +must -not prefix*

Aggregate

termsgroup by
date_histogramby day, week, month
histogram · range · date_rangebuckets
sum · avg · min · max
cardinalitydistinct count
value_count · stats · percentiles

Shape the result

sortany field, asc or desc
sourceonly the fields named
page_sizeup to 200
skipup to 10,000

Deep Query for AI agents. Faster, cheaper answers from your business data.

Deep Query use cases. Query any system of record
like a database.

Deep Query benchmark. Half the tokens
on the same tasks.

Tokens used

Tasks completed

Truncated responses

Faster tool responses. Read synced data
without waiting on the source API.

Hear it on a voice call

Configure Data Sync. Set how often Data Sync
refreshes your records.

Sync any list action, including your own.

Deep Query syntax. Filter, search and count
on any synced field.

Deep Query FAQ. How Deep Query works
with your AI agent.

Get started with Deep Query.

Deep Query for AI agents. Faster, cheaper answers from your business data.

Deep Query use cases. Query any system of record like a database.

Deep Query benchmark. Half the tokens on the same tasks.

Tokens used

Tasks completed

Truncated responses

Faster tool responses. Read synced data without waiting on the source API.

Hear it on a voice call

Configure Data Sync. Set how often Data Sync refreshes your records.

Sync any list action, including your own.

Deep Query syntax. Filter, search and count on any synced field.

Deep Query FAQ. How Deep Query works with your AI agent.

What is StackOne Deep Query?

How does Deep Query work with MCP?

Can an agent use Deep Query across different apps?

How does Deep Query help agents respond faster?

Can Deep Query filter fields the provider API cannot?

How does Deep Query reduce AI agent token costs?

How fresh are Deep Query results?

Is Deep Query a replacement for RAG?

How do I enable Deep Query?

Get started with Deep Query.

Deep Query use cases. Query any system of record
like a database.

Deep Query benchmark. Half the tokens
on the same tasks.

Faster tool responses. Read synced data
without waiting on the source API.

Configure Data Sync. Set how often Data Sync
refreshes your records.

Deep Query syntax. Filter, search and count
on any synced field.

Deep Query FAQ. How Deep Query works
with your AI agent.