What Data for This Category Looks Like
Footwear yield rate-related data primarily comes from domestic professional textile and apparel B2B wholesale platforms and cross-border e-commerce public sales datasets. Domestic offline wholesale stall quotation data is updated before 9 AM daily. Cross-border e-commerce platform selling price data is synchronized every 3 hours. Data is returned in structured JSON format. A single data entry includes the sku_id, goods_name, brand, supply_price, retail_price, and update_time fields. The units for supply_price and retail_price are Chinese Yuan per pair. update_time uses UTC timestamp format. No additional aggregated statistical fields are included.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
Data sources are divided into two categories: offline wholesale and cross-border e-commerce, corresponding to different interface addresses and authentication rules. As such, tool calling requires configuring multiple independent MCP service parameter sets. Differences in update frequencies require that calling intervals match the update rhythm of the corresponding data source, to avoid invalid requests or data lag behind the release time of daily yield rate reports. Structured fixed fields require that the tool return schema strictly matches the data source documentation. Otherwise, missing fields or format errors may occur, preventing the generation of accurate yield rate report content. Footwear SKU categories have many subdivisions. When pulling data in batches, the number of returned entries per request must be limited to prevent request timeouts that affect tool calling stability.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mcp_request_timeout | Offline data source: 30 seconds, Cross-border data source: 15 seconds | Offline interfaces typically have slower response times; cross-border interfaces return larger data volumes but faster responses |
tool_call_interval | Offline data source: 86400 seconds, Cross-border data source: 10800 seconds | Matches the update frequency of the corresponding data source to avoid invalid calls |
mcp_auth_type | Offline data source: api_key, Cross-border data source: none | Offline B2B platforms require key verification; cross-border public interfaces do not need authentication |
batch_fetch_max_size | 50 entries per request | When pulling footwear SKU data in batches, this returns a moderate volume of data to avoid timeouts |
tool_response_schema | Configured in the order sku_id, goods_name, supply_price, retail_price, update_time | Strictly matches the standard documentation structure of the data source to ensure field matching |
enable_cache | Enabled | Price data for the same SKU does not need to be re-pulled within its update cycle, reducing the number of interface calls |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. Testing on local deployment samples is recommended before finalizing configuration values.
Three Common Misconfigurations
- Symptom: Tool calling returns a
502 Bad Gatewayerror. Cause: MCP service communication protocol is not configured correctly. FastGPT uses the SSE protocol by default. If connecting to a local MCP service started with NPX, port forwarding or protocol conversion configuration is not added. - Symptom: The
supply_pricefield returned by the tool is empty. Cause: The data source field names are not matched correctly. Other field names are mistakenly used as configuration items, andtool_response_schemais not adjusted to match the actual structure of the data source. - Symptom: Tool calling frequently triggers timeouts. Cause: The calling interval for cross-border data sources is set to the same
86400 secondsas offline data sources.tool_call_intervalis not adjusted based on update frequency, resulting in an excessively large single request data volume.
How to Confirm Proper Configuration
- Navigate to the FastGPT tool management page, select the target footwear yield rate tool, click "Test Call", enter a valid SKU ID, and confirm the returned structured data includes the expected fields.
- View the tool's calling logs to confirm that each calling interval matches the configured
tool_call_interval, with no abnormal 4xx or 5xx error codes. - Check the tool's cache switch status, and confirm that when calling the same SKU repeatedly within its update cycle, the returned
update_timevalue does not change. - Switch between different data source configurations, test the calling results of offline and cross-border interfaces separately, and confirm that the authentication configuration takes effect, with no authentication failure prompts.
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.