What this category of data looks like
Data related to grain reserves is primarily sourced from daily inventory ledgers of state-owned reserve depots at all levels, warehouse temperature and humidity inspection records, and officially published grain reserve dispatch announcements. The update schedule is as follows: daily ledgers are synced daily, while official announcements are released every ten days. Each data document follows a structured format, including fields such as storage depot number, grain type, actual total inventory (unit: tons), inbound batch number, quality inspection qualification status, remaining storage period, and real-time warehouse temperature and humidity (unit: degrees Celsius, %RH). All fields have uniform formats, with no complex nested structures.
What constraints these characteristics impose on tool calling and plugins
Structured data sources with fixed fields require that the parameter schema for tool calling must strictly match preset fields, and no undefined parameters may be added. Daily updated inventory ledgers require that the cache duration for real-time query plugins must not exceed 24 hours, to avoid returning outdated inventory information. The presence of real-time temperature and humidity monitoring fields requires that frequently called tools set a reasonable call interval, to avoid exceeding the update frequency of monitoring data. Fixed value ranges for core query fields require that plugins validate the legality of input parameters before calling, filtering invalid query requests.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
functionCall | {"name":"get_grain_reserve_data","parameters":{"type":"object","properties":{"grain_type":{"type":"string"},"storage_location":{"type":"string"}},"required":["grain_type"]}} | Matches the core query fields of grain reserve data, strictly adhering to the field requirements of structured data |
toolChoice | auto | Allows the model to automatically select whether to call the reserve data tool based on user queries, adapting to different inquiry scenarios |
| Plugin Cache Duration | 12–24 hours | Aligns with the daily update rhythm of daily ledgers, avoiding the return of outdated data |
| Parameter Validation Switch | Enabled | Filters invalid inputs, reducing unnecessary tool calls |
| Call Frequency Limit | 5 requests per 10 seconds | Adapts to the update frequency of warehouse monitoring data, avoiding exceeding the data source's carrying capacity |
Three Common Configuration Mistakes
- Phenomenon: Only partial steps are executed when calling an associated workflow, with no complete return result. Cause: The result return node is not configured in the called workflow, so no return data is passed to the main workflow after execution completes.
- Phenomenon: An
Error: write EPROTerror is returned when calling a third-party interface. Cause: The plugin's HTTPS request verification parameters are not configured correctly, resulting in protocol handshake failure. - Phenomenon: The inventory data returned by the tool call has an incorrect unit, with kilograms and tons mixed up. Cause: No result unit conversion rule is specified in the plugin configuration, and unstandardized fields from the original data source are read directly.
How to Confirm Proper Configuration
- A simulated query can be initiated by entering a known grain type and storage location, and the returned data fields are verified to exactly match the preset structured fields.
- Plugin call logs are checked to confirm that the parameter validation switch is active, and invalid inputs are intercepted with clear prompts.
- After waiting longer than the configured cache duration, the same query is initiated again, and the returned data is verified to be updated to the latest inventory information.
- Multiple requests are initiated in a short period, and the system is confirmed to not trigger frequency restriction interception.
The values provided are common starting points and should be evaluated using samples specific to the deployment context.
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.