What the Data for This Category Looks Like
Home goods profit margin and market trend data comes primarily from monthly monitoring ledgers published by domestic light industry associations, and dealer pricing documents publicly released by leading home goods brands. There are two update schedules: regular categories are updated monthly, while popular SKUs have weekly monitoring data.
Each data document includes SKU code, category name, supply channel name, supply unit price, terminal sales unit price, profit difference, data collection date, and data source institution. The units for supply unit price and terminal sales unit price are yuan per piece. The unit for profit difference is yuan per piece. Data is not updated in real-time streaming. All data is archived after structured batch collection.
Constraints for Tool Calling and Plugins
The structured archiving property of the data requires that tool calling support batch queries or precise single-SKU matching. Real-time streaming interfaces cannot be used. The monthly and weekly update schedules limit reasonable tool call frequency. Frequent calls will trigger data source interface rate limiting or return duplicate data.
The field structure where SKU acts as the unique identifier requires valid SKU codes to be passed as query parameters during tool calling. Without a valid SKU code, profit margin data for the corresponding category cannot be located. The multi-dimensional associated field design requires strict matching between plugin and data source field mapping rules. Mismatched field names may cause calling failures.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
tool_call_timeout | 300 seconds | Structured queries for home goods data typically require integration with industry database interfaces, which have higher response delays than general-purpose tools |
plugin_compatibility_version | beta4 | Some public home industry data plugins are compatible with the beta4 version of the plugin framework |
mongodb_connection_timeout | 120 seconds | MongoDB collections for home goods category data usually contain multi-dimensional associated fields, which take longer to establish connections |
field_mapping_rule | Map core fields by SKU code | Home goods data uses SKU as the unique identifier, so data source field names must be strictly matched |
tool_call_frequency_limit | 1 time per hour | Home goods market trend data is updated monthly or weekly. Frequent calls are unnecessary and may trigger interface rate limiting |
plugin_request_retry_times | 2 times | Public industry data interfaces occasionally experience temporary fluctuations. A small number of retries can improve call success rates |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Tool calls return
field is emptyordata not updated. Cause: The settool_call_frequency_limitis higher than the actual update schedule of the home goods category data. This causes calls to fail to retrieve the latest archived data. - Symptom: Plugin validation throws
MongoServerError: The dollar ($) prefix is not allowed in field names. Cause: Field names containing the $ symbol are used in the MongoDB collection, and no escape rule is configured infield_mapping_rule. The MongoDB driver for beta4 version plugins has stricter field name validation. - Symptom: Markdown to file plugin calls show
file upload failed. Cause:UPLOAD_FILE_MAX_SIZEis not configured to accommodate the long document output of home goods category data, or correct file storage path permissions are not specified.
How to Verify Proper Configuration
- Run a tool call test. Pass a known home goods SKU code, and verify that the returned field structure matches the data source document.
- Check plugin runtime logs. Confirm that there are no MongoDB connection timeout errors, and that the field mapping rules are active.
- Adjust the tool call frequency. Verify whether rate limiting or duplicate data returns are triggered. Adjust the frequency limit based on the actual data update schedule.
- Call the Markdown to file plugin. Verify that the generated file format and output content match. Confirm that the upload path permissions are correct.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.