What this category’s data looks like
Bulk commodity trade ledger data mainly comes from enterprise internal ERP systems, port delivery vouchers, spot settlement documents, and futures delivery records. Data is updated in sync with transaction batches or on a daily basis. Individual ledger documents are structured tables containing core fields such as commodity name, specification model, origin, transaction quantity (unit: ton, barrel, or cubic meter), tax-included unit price, transaction date, payer/receiver entity information, and settlement status. Some ledgers also include quality inspection report numbers and logistics tracking numbers.
What constraints do these characteristics impose on tool calling and plugins
The multi-field and multi-unit characteristics of bulk commodity trade ledgers require pre-configured field mapping rules during tool calling. These rules unify units from different sources into standard units of measurement. Frequently updated transaction data requires the plugin’s synchronization interval to match the business update rhythm. This avoids data lag caused by too low a call frequency. Structured ledger fields and fixed enumeration values such as settlement status require tool calling parameters to strictly match preset enumerations. This prevents invalid parameter input. At the same time, single-batch ledger data has a large volume. The number of items returned by a single tool call must be limited to avoid context overflow.
Configuration guidelines
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Automatically match multiple types of data extraction tools required for bulk commodity ledger summary |
| Single tool call entry limit | Top 15 entries | Limit the scale of data returned per batch to adapt to context window restrictions |
| Timeout period | 240 seconds | Adapt to the organization and format conversion duration of bulk ledger data |
functionCall | Strict mode | Force the large model to output structured results that conform to preset formats, matching ledger field requirements |
| Stream output control | Disabled | Ensure a complete summary report is generated before returning, avoiding incomplete reports caused by segmented output |
| Field unit unification rule | Calibrated based on actual testing | Adapt to conversion requirements for multiple units such as tons, barrels, and cubic meters in bulk commodity trade |
Three common configuration errors
- Phenomenon: When calling the MCP service, the type of custom parameters is forcibly restricted to string, and numeric or enumeration types cannot be passed. Cause: Custom parameter type verification is not enabled in the plugin configuration, causing FastGPT to convert all parameters to string format by default.
- Phenomenon: The large model returns empty responses consecutively, without any ledger data or report content. Cause: The strict mode of
functionCallis not configured correctly, causing the large model to fail to match the preset ledger field format, triggering an empty return. - Phenomenon: The number of ledger entries returned after calling the tool does not match expectations, exceeding or falling below the set limit. Cause: The single tool call entry limit is not set correctly, or the return quantity is not restricted in the plugin logic, leading to data overflow or incompleteness.
How to confirm the configuration is complete
- Initiate a simulated call, pass test data for a single bulk commodity ledger, and check whether the tool returned fields include preset core content such as commodity name and transaction quantity.
- View the tool call log to confirm that parameter types are not forcibly converted to string, meeting preset configuration requirements.
- Trigger the complete ledger summary process, and check whether the final generated report includes all incoming ledger entries, with no gaps or missing content.
- Verify that the large model response is in structured format, conforming to the preset requirements of
functionCall.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.