BigSeller MCP Use Case Guide

The example below — "automated daily business report push" — shows how to set up a scheduled task, have AI call the BigSeller MCP to analyze and summarize your data, and push the result to a WeCom group.

Case 1: Automated Daily Business Report Push

From scheduled trigger to the report landing in your group chat — here is a complete, ready-to-implement path.

Goal: Every day at 09:00, automatically pull the previous day's BigSeller business data, have AI generate a concise business report, and push it to a designated WeCom group. A regular chat window does not run on its own without someone operating it, so you need an AI Agent with scheduled-task support, an automation platform, or an external scheduler to trigger the task.

Step 01 / Preparation: Get Four Things Ready

• BigSeller MCP is connected in your AI client and can query data normally.

• An AI Agent or automation platform that can run scheduled tasks.

• A WeCom group and the group robot's webhook URL.

• Report recipients, execution time, data time range, and the metrics to watch.

Step 02 / Task Setup: Recommended Scheduled Task Configuration

• Task name: BigSeller Daily Business Report

• Frequency: Once a day

• Execution time: 09:00 Beijing time (GMT+8)

• Data range: Previous calendar day, 00:00:00 to 23:59:59

• Output: Generate a Markdown report and push it to the WeCom group

Step 03 / AI Prompt for the Scheduled Task

Put the following into the scheduled task's "task description", "execution instructions", or "prompt" field. After the first-time setup, run it once manually to confirm the data range and report format.

You are the BigSeller daily business report assistant. Run the following task every day at 09:00 Beijing time:

1. Call the BigSeller MCP to query the previous calendar day's (Beijing time 00:00:00 to 23:59:59) orders, profit reports, and wallet transactions.
2. Summarize order count, sales revenue, refunded or cancelled orders, profit, platform commissions, shipping fees, and other fees by platform and by store.
3. Identify the top 5 SKUs by sales revenue and the bottom 5 SKUs by profit, and explain the possible reasons that need attention.
4. If data for the previous day or the last 7 days is available, make day-over-day or week-over-week comparisons; if the data is insufficient, state explicitly "no comparable data" — do not guess.
5. Organize the results into a Markdown report suitable for reading in a WeCom group, with a fixed structure: Data Overview, Platform/Store Performance, Profit & Fees, Key SKUs, Anomalies & Recommendations.
6. Every amount, quantity, and date in the report must state its basis; for metrics with no data, display "No data".
7. After the report is generated, call the configured WeCom group robot to push the report. Use the title "BigSeller Daily Business Report | Date: YYYY-MM-DD".

Step 04 / AI Analysis Process: Process the Data in a Fixed Order

1. Call the MCP to fetch raw business data and confirm the statistics date returned.
2. Standardize fields such as platform, store, SKU, amount, and order status.
3. Calculate core metrics: order volume, sales revenue, refund rate, profit, and fees.
4. Sort by platform, store, and SKU to identify high sales, low profit, or anomalies.
5. Convert the analysis into a fixed-format Markdown report so the output structure stays consistent.

Step 05 / Manual Test Run: Run It Once After First-Time Setup

Before the scheduled task goes live, send the message below in the same AI Agent to verify the full pipeline: MCP query, AI analysis, and push.

Run the BigSeller daily business report task immediately. Use the previous calendar day's data, call the BigSeller MCP to analyze orders, profit, and wallet transactions, generate a Markdown report, and push it to the configured WeCom group.

Step 06 / Configure the WeCom Group Push

Option A — Use the platform's built-in WeCom node:

1. In the scheduled task, add a "Send to WeCom", "WeCom group robot", or similar push action.
2. Enter the WeCom group robot webhook URL.
3. Map the AI-generated report variable, such as report_text, to the message content.
4. Select the Markdown message format, save, and send a test message.

Option B — If only an HTTP request node is available:

Call the WeCom group robot as an HTTP webhook, usually with a POST request, and put the AI-generated report into markdown.content.

POST https://
Content-Type: application/json

{
  "msgtype": "markdown",
  "markdown": {
    "content": "## BigSeller Daily Business Report\nDate: {{report_date}}\n\n{{report_text}}"
  }
}

Step 07 / Sample Report in the WeCom Group

## BigSeller Daily Business Report
Date: 2026-09-20

### 1. Data Overview
- Orders: 1,286
- Sales revenue: ¥86,420
- Refunded/cancelled orders: 43
- Order profit: ¥12,680

### 2. Platform & Store Performance
- Shopee: 760 orders, sales ¥48,300, profit ¥7,120
- TikTok Shop: 382 orders, sales ¥25,600, profit ¥3,410
- Lazada: 144 orders, sales ¥12,520, profit ¥2,150

### 3. Key SKUs
- Highest sales: SKU-A001
- Lowest profit: SKU-B208 — check purchase cost, platform commissions, and shipping fees

### 4. Anomalies & Recommendations
- Refunds on TikTok Shop rose versus the previous day; review the affected products and after-sales reasons.
- SKU-B208 sells normally but at low profit; re-check its selling price and fulfillment costs.

End-to-End Flow

Scheduled trigger → AI reads the task prompt → Calls BigSeller MCP → Analyzes and summarizes → Generates Markdown → WeCom webhook → Report lands in the group