What the data for this category looks like
Data sources for textile manufacturing due diligence include customs import and export declarations, upstream textile raw material spot quotation platforms, factory ERP production ledgers, supply chain fulfillment documents, and third-party quality inspection reports. Raw material quotation data updates daily. Production ledgers sync daily. Declarations and fulfillment documents update weekly. Quality inspection reports update in real time with production batches. Most data documents use structured CSV or JSON formats. Some quality inspection reports use PDF format. Fields include purchase order number, raw material batch number, yarn count specification (unit: Ne/tex), loom operating rate (unit: machines/shift), delivery cycle (unit: days), breaking strength (unit: N/tex), shrinkage rate (unit: percentage), and others.
What constraints these characteristics impose on tool calling and plugins
The daily update requirement for raw material quotations means tool calls must set a reasonable pull interval to avoid outdated data. Differences in multiple units require configuring unified unit conversion rules during tool calls. Without this, returned data cannot adapt to standardized display. The large volume of multi-batch ledger data requires tool calls to support paging and batch filtering, and filter data using raw material batch number as the unique identifier. Some PDF quality inspection reports require calling a file parsing plugin. Parsing rules adapted to textile manufacturing quality inspection reports must be configured to ensure field extraction accuracy.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Quality inspection PDFs for textile manufacturing usually contain multi-page batch data, with long parsing time. 300 seconds covers most scenarios |
MCP_TOOL_TIMEOUT | 120 seconds | Raw material spot quotation interface calls have high frequency. Too short a timeout will trigger invalid retries, too long will block the workflow |
tool_call_batch_size | 50 items | Single batch data volume of textile manufacturing purchase ledgers does not exceed 50 items, avoiding excessive data volume in a single call |
field_mapping | Map to "raw material batch number", "yarn count specification", "delivery cycle" | Unify differences in field names across data sources, adapt to standardized fields for due diligence reports |
unit_conversion_enabled | Enabled | Unify unit conversion between Ne and tex, adapt to unified standards for report display |
api_debug_mode | Enabled during debugging, disabled in formal operation | Capture call logs to troubleshoot issues; reduce log overhead in formal environments |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: MCP tool calls return timeout errors with status code 504. Cause: The
MCP_TOOL_TIMEOUTparameter is not adjusted, and the default timeout period is too short to cover the pull time of textile manufacturing data. - Phenomenon: Workflow calls return the "invalid workflow id" error. Cause: The unique identifier from the workflow management page is not copied correctly, or an incorrect ID parameter is passed when calling the interface.
- Phenomenon: The text stream returned by API calls is only continuous plain text with no structured format. Cause: The markdown output switch for tool calls is not enabled, or a text formatting plugin is not added, failing to convert textile manufacturing indicator data into table form.
How to confirm configurations are set correctly
- Enter the FastGPT tool management page, check that the
MCP_TOOL_TIMEOUTconfiguration value is 120 seconds, which matches the preset range. - Submit a call request for textile manufacturing purchase ledger data, confirm that the returned field names match the
field_mappingconfiguration, and units have been uniformly converted. - Copy the unique identifier from the workflow management page, pass it to the API call interface, and confirm that the returned status code is 200 with no parameter errors.
- View the tool call logs, confirm that the returned text stream contains markdown-formatted tables and indicator descriptions, meeting the display requirements of due diligence reports.
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.