What the data for this category looks like
For brand agency operation intelligent due diligence reports targeting the financial sector, data comes from authorized documents provided by brands, e-commerce store backends, social media account backends, and third-party monitoring tools. There are two data update cycles. Basic qualification data such as brand authorization and compliance documents is updated quarterly. Operational and public opinion data such as store visitors and content interaction volume is updated daily or hourly. The document structure includes four modules: basic qualifications, operational data, public opinion monitoring, and compliance verification. Fields include daily average store visitor trips, weekly content release frequency, average interaction count per post, valid cooperation authorization days, and more. Some fields are paired with units corresponding to their business scenarios, such as trips, posts/week, and times/post.
What Constraints These Characteristics Impose on Tool Calling and Plugins
The due diligence data for brand agency operations has scattered sources, varying update cycles, and fields associated with business units. This creates three types of constraints for tool calling and plugin configuration. First, multiple data sources have varying API formats and authentication methods. Configure independent request headers, authentication parameters, and response parsing rules for each data source. This prevents call failures caused by format mismatches. Second, static qualification data and dynamic operational/public opinion data have different update cycles. Differentiate the scheduled task frequency for triggering tool calls. This avoids redundant calls that waste resources or data lag that reduces due diligence accuracy. Third, the association between fields and business units requires automatic binding of corresponding unit identifiers during tool calls. This prevents reduced report readability caused by mismatched fields and units in output results.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PLUGIN_API_TIMEOUT | 120–180 seconds | Most data source interfaces for brand agency operations involve multi-round pulls, with long single-request latency. This range prevents call interruptions due to timeouts |
INCREMENTAL_SYNC_ENABLE | Enabled | Dynamic operational and public opinion data has a high update frequency. Incremental sync reduces resource consumption from repeated pulls while ensuring data timeliness |
PARSE_FILE_FIELD_MATCH | Exact match by field name | Due diligence report fields are associated with business units. Exact matching prevents binding errors between fields and units |
UPLOAD_FILE_MAX_SIZE | 500 MB | Qualification documents and bulk operational data files for brand agency operations typically do not exceed this size, adapting to common file upload limits |
TOOL_CALL_RETRY_TIMES | 2–3 times | Multi-source data interfaces may experience temporary failures due to network fluctuations. Limited retries improve call success rates |
API_REQUEST_HEADERS | Configure independent authentication headers per data source | Authentication methods differ across third-party data sources. Independent configuration prevents authentication failures |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: The CSV file set created via API calls does not match the data fields generated in the interface. Cause:
PARSE_FILE_FIELD_MATCHis not set to exact match mode. This leads to inconsistent field mapping between automatic API calls and manual configuration rules. - Symptom: HTTP requests for tool calls fail to pass file-format parameters properly, returning a 400 error code. Cause:
Content-Typeis not correctly configured asmultipart/form-datainAPI_REQUEST_HEADERS. The data source does not recognize the request format. - Symptom: After upgrading to version 4.8.10, tool calls from the original application stop working, with an error stating "corresponding tool not found". Cause: The original application configuration was not migrated to a team plugin and added to the tool calling list. Only the original application configuration was retained, and migration was not completed.
How to Verify Configurations Are Correct
- Call the test interface for a single data source, and verify that returned fields match the configured parameter mapping rules.
- Upload a single brand qualification document, and check that parsed fields are bound to the correct business units.
- Review tool calling logs to confirm that retry counts and timeout settings align with data source characteristics.
- Trigger a scheduled task, and compare the update range of manually pulled data and automatically pulled data to ensure it matches business requirements.
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.