What data looks like for this category
Aquaculture financial report data comes primarily from internal production ledgers of aquaculture enterprises, monthly statistical reports from local aquatic technology promotion stations, and third-party aquatic industry monitoring databases. Data update follows a three-tier rhythm:
- Monthly pond production data is synced daily
- Quarterly summary reports are released at the end of each quarter
- Full annual reports are updated within January of the following year
Documents use structured tables as their core format, with fields including aquaculture water area, seedling stocking volume, feed usage, finished product slaughter volume, yield per unit area, unit production cost, and more. Most field units are mu, ten thousand tails, tons, kg/mu, and yuan/mu.
What constraints do these characteristics impose on tool calling and plugins
Multi-source scattered data for aquaculture financial reports requires tool calling to connect internal enterprise ERP plugins, public industry data APIs, and third-party purchased data interfaces simultaneously. Multi-node parallel calls must be configured.
Seasonal production cycle characteristics require tools to support filtering parameters by breeding batch and quarterly cycle, to avoid pulling data from irrelevant time periods.
Specific field units and structured segment formats require preset field mapping rules during tool calling. This automatically converts raw data units to the unified format required for financial reports. It also requires validation of field legitimacy to prevent incorrect area and weight parameters from being passed.
Monthly high-frequency updated data requires tool call trigger frequencies to align with business rhythms, to avoid excessive calls or missing the latest data.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MCP_SERVICE_LOG_LEVEL | debug | Aquaculture financial reports have a large number of fields with special formats. Debug-level logs can accurately locate exceptions during field parsing and parameter transfer |
HTTP_TOOL_ALLOW_FILE_INPUT | enabled | Aquaculture financial reports often include attachments such as pond photos and water quality test reports. This setting supports passing file-type input parameters |
WORKFLOW_TRIGGER_INTERVAL | 86400 seconds | Monthly aquaculture production data is updated daily. This configuration ensures that the latest pond production data is pulled daily for financial report summarization |
TOOL_CALL_MAX_RETRIES | 3 retries | Public industry data interfaces for the aquaculture sector are subject to a high probability of network fluctuations. A retry mechanism reduces call failure rates |
SESSION_ID_PASS_THROUGH | enabled | Session context for multiple breeding batches of data must be retained, to avoid losing historical parameter configurations when calling tools repeatedly |
RAG_RECALL_CHUNK_SIZE | 1000–1200 characters | Individual aquaculture records in aquaculture financial reports are relatively long. This configuration adapts to the length of documents stored in segments, avoiding content truncation |
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. Testing on internal samples is recommended before finalizing settings.
Three Common Mistakes
- Phenomenon: Empty fields are returned after calling the MCP service, with a status code of 200. Cause: The exclusive field mapping for aquaculture financial reports is not configured. Raw data field names returned by the tool do not match workflow preset fields such as
breeding areaandslaughter volume, leading to parsing failure. - Phenomenon: HTTP tool call returns error
413 Request Entity Too Large. Cause: TheHTTP_TOOL_ALLOW_FILE_INPUTconfiguration is not enabled, or the uploaded aquaculture test report file size exceeds the default limit ofUPLOAD_FILE_MAX_SIZE. - Phenomenon: The workflow cannot associate historical session breeding data during operation, and the LLM return result does not include context information. Cause: The
SESSION_ID_PASS_THROUGHconfiguration is not enabled, and the session identifier is not passed to the LLM tool node in the workflow, leading to context loss.
How to Confirm Configuration is Correct
- Enter the FastGPT workflow debug panel, trigger an MCP tool call, and view the detailed logs generated by the
MCP_SERVICE_LOG_LEVELconfiguration. Confirm that the raw field parsing process for aquaculture financial reports is included. - Upload a PDF file of an aquaculture-related test report to the HTTP tool node. Confirm successful upload and that the tool returns processed text content without format errors.
- Start a workflow test, and check the input parameters of the LLM tool node. Confirm that the session ID has been correctly passed to the context parameters.
- Adjust the
RAG_RECALL_CHUNK_SIZEconfiguration, test whether the recalled document fragment length matches the preset range, and confirm that no truncation or over-length situations occur.
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.