What this category’s data looks like
Aquaculture intelligent due diligence report data comes from three categories:
- Real-time data from IoT terminals deployed at ponds, such as water quality monitors and feeders
- Paper and electronic records from aquaculture operators, including seedling batches, feeding logs, and disease prevention and control records
- External documents such as aquatic seedling quarantine certificates and purchase quotation sheets
Update frequencies vary significantly:
- Real-time water quality parameters are updated minute-by-minute
- Feeding and disease records are updated daily
- Batch-level quarantine and sales data are updated after each batch is completed
Document structure is divided into four modules:
- Basic information module: pond coordinates, breeding area, operator qualifications
- Water quality parameter module: dissolved oxygen, pH value, ammonia nitrogen content
- Production record module: feeding amount, medication status
- Sales module: purchase price, shipment volume
Clear specifications apply to fields and units:
- Dissolved oxygen and ammonia nitrogen content are measured in mg/L
- pH value has no unit
- Feeding amount is measured in kg/mu/day
- Seedling count is measured in individual units
What constraints these characteristics impose on tool calling and plugins
Multi-source heterogeneous data sources require tool calling to connect to multiple types of data source plugins, including IoT device API plugins, ledger database plugins, and document parsing plugins.
Minute-by-minute real-time data updates require tool calling frequencies to match the data update rhythm, to avoid triggering API rate limits from frequent pulls.
Differences in field units across modules require unit conversion during tool calling. Without this, due diligence report data will become disorganized.
Batch-level data update logic requires tool calling to filter data by breeding batch, to avoid mixing data across batches.
Additionally, due diligence reports require integration of multi-source data before generation. The context of tool calling must retain the association relationship of each data source to prevent data misalignment.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
TOOL_TIMEOUT_SECONDS | 300–600 seconds | Aquaculture data requires integration of multi-source heterogeneous content, and a single tool call must complete multiple types of data pulling and format conversion. A setting of 300 seconds or longer covers normal operation durations |
MCP_PARAM_TYPE_AUTO_DETECT | Enabled | Aquaculture data includes multiple parameter types such as numeric values (dissolved oxygen in mg/L) and text (disease descriptions). Automatic detection prevents parameters from being fixed to the string type |
MAX_CONTEXT_TOOL_CALLS | 3–5 times | A single due diligence report requires integration of 3 to 4 types of data sources. Excessive calls will cause context overflow and affect subsequent generation logic |
DISABLE_STREAM_TOOL_OUTPUT | Enabled as needed | Disabling stream output when generating due diligence reports in bulk prevents content truncation. Keep it enabled for real-time generation scenarios |
TOOL_OUTPUT_PARSE_STRICTNESS | Permissive mode | Aquaculture ledgers contain handwritten records in non-standard formats. Permissive mode reduces parsing failure rates |
MCP_SERVICE_RETRY_COUNT | 2 times | IoT monitoring device APIs have occasional fluctuations. Limited retries improve call success rates |
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, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Tool call returns an empty response with no valid content. Cause: The number of tool calls per round exceeds the threshold set by the
MAX_CONTEXT_TOOL_CALLSconfiguration, or the passed parameter format does not meet the requirements of the data source API. - Symptom: Numeric parameters returned by the MCP service are forcibly converted to the string type. Cause: The
MCP_PARAM_TYPE_AUTO_DETECTconfiguration is not enabled, and the deployed version is 4.9.6, which has the characteristic of fixing all parameter types to string. - Symptom: Tool call nodes in the workflow cannot reference knowledge base recall results. Cause: The recall output variables of the knowledge base are not bound to the input parameters of the tool call node, and the context association is not completed.
How to confirm correct configuration
- Access the tool call configuration page, check if
MCP_PARAM_TYPE_AUTO_DETECTis enabled. Use a test account to initiate a dissolved oxygen data pull, and verify that the returned parameter type is numeric. - Initiate a single due diligence report generation task, check the tool call logs to confirm that the number of calls does not exceed the threshold set by the
MAX_CONTEXT_TOOL_CALLSconfiguration. - Test disabling the stream output function, generate bulk due diligence reports, and check that the output content is complete and not truncated.
- Bind the knowledge base to the tool call node, initiate a test call, and check that the logs contain relevant content from the knowledge base recall.
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.