Get data usage overview and trends

Tool Name: airdroid_business_get_data_usage_overview_and_trends

Risk Level: 🟢 Low Risk

Execution Mode: ⚡ Synchronous

Category: Reports & Analytics

Quick Start (Copy & Use)

In one sentence: Get device data usage overview, trends, and Top 10 device traffic data

What you need: StartDate (start date), EndDate (end date)

Success criteria: StatusCode=200 and ErrorMessage="" and FlowData contains the corresponding data structure

What to do next: Analyze data usage trends, or use Get Top 10 Data Usage Apps to see specific app data consumption

Minimal request example:

{
  "StartDate": "2026-01-01",
  "EndDate": "2026-01-31",
  "ReportType": 1
}

Minimal response example:

{
  "FlowData": {
    "title_total": {
      "total_flow": "2506.89",
      "avg_flow": "2506.89"
    },
    "trend_total": {
      "total": "856.32",
      "list": {
        "2026-01-01": {
          "wifi_count": "45.25",
          "mobile_count": "12.30",
          "avg_count": "1.15",
          "total_count": "57.55"
        }
      }
    },
    "top_total": {
      "device": [
        {
          "device_id": "abc123def456",
          "device_name": "Sales Phone 01",
          "total_count": "125.50",
          "wifi_count": "100.25",
          "mobile_count": "25.25",
          "tags": []
        }
      ]
    }
  },
  "StatusCode": 200,
  "ErrorMessage": ""
}

Recipes (Common Recipes)

Recipe 1: Get Full Data Usage Report (Overview + Trends + Top Devices)

Scenario: Comprehensive understanding of data usage

{
  "StartDate": "2026-01-01",
  "EndDate": "2026-01-31",
  "ReportType": 1,
  "FlowType": 1
}

Recipe 2: Get Data Usage Overview Only

Scenario: Quick view of summary statistics

{
  "StartDate": "2026-01-01",
  "EndDate": "2026-01-31",
  "ReportType": 4,
  "FlowType": 1
}

Recipe 3: Get Data Usage Trend Data

Scenario: Analyze data usage change trends for a specific date range

{
  "StartDate": "2026-01-01",
  "EndDate": "2026-01-31",
  "ReportType": 2,
  "FlowType": 1
}

Recipe 4: Get Device Traffic Statistics

Scenario: Find devices with the highest data consumption

{
  "StartDate": "2026-01-01",
  "EndDate": "2026-01-31",
  "ReportType": 3,
  "FlowType": 1
}

Recipe 5: Filter by Data Type (Mobile Data Only)

Scenario: Only count mobile data usage

{
  "StartDate": "2026-01-01",
  "EndDate": "2026-01-31",
  "ReportType": 1,
  "FlowType": 3
}

1. Overview

1.1 Description

Retrieve multi-dimensional device data usage, including overview statistics, daily trends, and device traffic rankings. Supports filtering by report type, group, and data type.

1.2 When to Use

• Data cost analysis: Understand overall organizational data consumption
• Trend monitoring: Track changes in data usage trends
• Anomaly detection: Identify devices with abnormally high data consumption
• Network planning: Optimize network configuration based on WiFi/mobile data usage ratios

1.3 Execution Mode & Response

This is a synchronous operation that returns results immediately upon invocation.

• Return value: FlowData object containing data usage statistics
• Data scope: Returns different data structures based on ReportType

1.4 Prerequisites

1.5 Prerequisite Tools

2. Inputs

2.1 Parameter List

2.2 Parameter Details

`StartDate` and `EndDate`

• Type: string
• Format: YYYY-MM-DD (e.g., 2026-01-01)
• Constraints:
  • Cannot be a future date
  • Date range cannot exceed 90 days

`ReportType`

• Type: number
• Options:

• Example:

  1

`GroupIds`

• Type: number-array
• How to obtain: Via the Get a Group ID by Group Name tool
• Example:

  [100, 101]

`FlowType`

• Type: number
• Options:

• Example:

  1

2.3 Parameter Combination Logic

• ReportType determines the returned data structure
• GroupIds and FlowType are independent filter conditions

3. Outputs

3.1 Response Examples

Success response (ReportType=1, full data):

{
  "FlowData": {
    "title_total": {
      "total_flow": "2506.89",
      "avg_flow": "2506.89"
    },
    "trend_total": {
      "total": "856.32",
      "list": {
        "2025-01-01": {
          "wifi_count": "45.25",
          "mobile_count": "12.30",
          "avg_count": "1.15",
          "total_count": "57.55"
        },
        "2025-01-02": {
          "wifi_count": "52.10",
          "mobile_count": "8.45",
          "avg_count": "1.21",
          "total_count": "60.55"
        }
      }
    },
    "top_total": {
      "device": [
        {
          "device_id": "abc123def456ghi789jkl012mno345pq",
          "device_name": "Test Device 01",
          "total_count": "125.50",
          "wifi_count": "100.25",
          "mobile_count": "25.25",
          "tags": [
            {
              "tag_id": 1,
              "name": "Sales"
            }
          ]
        }
      ]
    }
  },
  "StatusCode": 200,
  "ErrorMessage": ""
}

Error response:

{
  "FlowData": {},
  "StatusCode": -1,
  "ErrorMessage": "Invalid start_date. Must be a non-empty string."
}

3.2 Field Descriptions

title_total (Overview statistics — returned when ReportType=1 or 4):

trend_total (Trend data — returned when ReportType=1 or 2):

top_total (Device traffic statistics — returned when ReportType=1 or 3):

3.3 Data Unit Notes

Important: All data usage values are in MB (megabytes) and are string type.

Conversion formulas:

• To GB: parseFloat(value) / 1024
• To KB: parseFloat(value) * 1024

4. Examples

4.1 Basic Example: Get Full Data Usage Report

Request parameters:

{
  "StartDate": "2026-01-01",
  "EndDate": "2026-01-31",
  "ReportType": 1,
  "FlowType": 1
}

4.2 Advanced Example: Filter by Group for Mobile Data Only

Request parameters:

{
  "StartDate": "2026-01-01",
  "EndDate": "2026-01-31",
  "ReportType": 1,
  "GroupIds": [100],
  "FlowType": 3
}

4.3 Error Example: Empty Dates When ReportType=2

Request parameters:

{
  "StartDate": "",
  "EndDate": "",
  "ReportType": 2
}

Response:

{
  "FlowData": {},
  "StatusCode": -1,
  "ErrorMessage": "Invalid start_date. Must be a non-empty string."
}

5. Error Handling

5.1 AB Three-Layer Error Codes

5.2 Common Errors

5.3 Agent Self-Healing Decision Table

6. Best Practices

6.1 Performance Optimization

• Choose the appropriate ReportType: Only retrieve the data type you need
  • Overview only: Use ReportType=4
  • Trends only: Use ReportType=2
  • Device statistics only: Use ReportType=3
• Use group filtering: Reduce data volume for faster response
• Set reasonable date ranges: Smaller date ranges yield faster responses

6.2 Security Notes

🟢 Read operation: No special security requirements

6.3 Data Interpretation Tips

• All data usage values are string type and need to be converted to numbers for calculations
• trend_total.list is an object with date strings as keys
• top_total.device returns a list of devices sorted by total data usage

7. Related Tools

7.1 Prerequisite Tools

7.2 Follow-up Tools

7.3 Similar Tools Comparison

8. Tool Chains

8.1 Data Usage Anomaly Analysis Pattern

Scenario: Identify devices and apps with abnormal data consumption

Tool chain:

Get Data Usage Overview and Trends (ReportType=3) → Get Top 10 Data Usage Apps → Comparative analysis

Steps:

1. Call this tool to get device traffic statistics
2. Call Get Top 10 Data Usage Apps to get Top 10 data usage apps
3. Analyze whether data consumption is concentrated on specific devices or apps

8.2 Data Usage Trend Monitoring Pattern

Scenario: Monitor data usage trend changes

Tool chain:

Get Data Usage Overview and Trends (ReportType=2) → Data visualization/alerting

Steps:

1. Periodically call this tool to get trend data
2. Analyze the daily data in trend_total.list
3. Set threshold alerts (e.g., daily data usage exceeding 2x the average)