What the data for this category looks like
Core data sources for aquaculture include pond IoT sensors, breeding ledger systems, feed feeding record systems, and seed batch archives. Data updates follow three frequencies: real-time (for water quality indicators such as dissolved oxygen and water temperature), daily (for feeding amounts and medication records), and monthly (for seed growth data). Most document structures use structured fields, including standardized fields such as dissolved oxygen (unit: mg/L), water temperature (unit: ℃), feeding amount (unit: kg/mu), and disease type. The structure also includes unstructured content such as pond photos and breeding logs.
What constraints do these characteristics impose on tool calling and plugins
The multi-dimensional data characteristics of aquaculture create clear constraints for tool calling and plugin configuration. Differences in regional naming of structured fields require custom field mapping before tool calling to avoid parameter mismatches. The short update cycle of real-time data requires tool call intervals to match sensor reporting rhythms, to avoid obtaining expired data. Unstructured disease photos require binding a dedicated image recognition plugin adapted to aquaculture disease classification tags. General-purpose models cannot support accurate recognition for this use case. When processing data from multiple ponds in batches, bind the corresponding data source by pond ID to avoid mixing data across ponds.
How to set the configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
TOOL_FIELD_MAPPING_ENABLE | Enable + custom mapping rules | Regional naming differences exist in aquaculture ledger fields, so custom mapping is required to adapt to local fields |
TOOL_CALL_INTERVAL | 10-60 seconds | The reporting cycle of real-time water quality sensors mostly falls within this range, to avoid frequent calls exceeding interface quotas |
BATCH_EXECUTE_BIND_KEY | Pond ID | When processing data from multiple ponds in batches, bind the corresponding data source by the unique identifier to avoid data cross-use |
IMAGE_PLUGIN_MODEL_TYPE | Aquaculture disease-specific model | General image recognition models cannot accurately identify aquaculture-specific disease tags, so a dedicated plugin must be bound |
HIDE_TOOL_INPUT_OUTPUT | Only hide original call logs | Marketing content needs to display core breeding data, and there is no need to expose the original input and response details of tool calls |
TOOL_CALL_TIMEOUT | 300 seconds | Parsing high-definition pond photos and batch data takes a long time, so sufficient processing time must be reserved |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: An error is returned after running the configured database connection plugin, with the message
500 Internal Server Error. Cause: The field names of the aquaculture ledger database have not been mapped to the preset fields for tool calling, resulting in parameter mismatches. - Phenomenon: The complete
inputandresponsefield contents are displayed in the tool call output. Cause: TheHIDE_TOOL_INPUT_OUTPUTconfiguration item is not enabled, causing the original call logs to be exposed. - Phenomenon: When calling a workflow via API, the batch execution node does not complete all cycles, but online debugging executes normally. Cause: The API request does not carry the pond ID list parameters required for batch looping, or a reasonable
batchLoopTimeoutthreshold is not set.
How to confirm the configuration is complete
- Initiate a tool call request for a single pond, and verify that the fields in the returned data match the fields in the local breeding ledger.
- View the tool call logs to confirm that only the core data required for marketing is displayed, and the original call log content is not exposed.
- Initiate a batch call request, and verify that the number of returned data matches the number of incoming pond ID lists.
- Trigger an image recognition plugin call, and verify that the returned disease classification results match the actual disease status of the pond.
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.