What the data for this category looks like
Refinery yield rate-related data for financial and wealth management scenarios is primarily sourced from third-party commodity financial market APIs, refinery manufacturing execution systems (MES), and real-time device monitoring systems. Market data updates every 15 minutes. Device operation data synchronizes every hour. A complete version of the combined daily yield rate report dataset is generated every hour. The data uses a structured format, including fields such as timestamp, device ID, raw material type, raw material purchase cost, each refined product output volume, each product sales unit price, and unit operation energy consumption. Corresponding units are hour, ton, yuan/ton, kg standard coal/ton, etc. There are no complex nested levels.
Constraints on Tool Calling and Plugins From These Characteristics
Since data sources are scattered and update schedules differ, tool calling must connect to multiple data sources in parallel and align timestamps. This prevents data timing errors from affecting the accuracy of yield rate calculations. Multiple fields with inconsistent units require plugins to include built-in field mapping and unit conversion logic. This ensures refinery data from different sources can be unified and integrated. Fixed update frequencies limit the trigger interval for tool calling. Frequent calls to unchanged data sources should be avoided. Although the structured, flat data structure is easy to parse, specific field filter conditions must be specified in tool configuration. This prevents redundant data from returning and consuming context quota.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
tool_call_timeout | 300 seconds | Refinery data pulling requires connecting to multiple data source types. 300 seconds covers normal response durations for most APIs, preventing call interruptions due to timeout |
max_tool_calls_per_round | 3–5 times | A single round of yield rate calculation requires calling multiple tools including market data, device energy consumption, and product pricing. This range balances calculation completeness and context overhead |
tool_response_filter_fields | Device Number, Timestamp, Raw Material Cost, Product Selling Price, Unit Energy Consumption | Only retain fields required for yield rate calculation, filter redundant data to save context quota |
tool_call_streaming | false | Complete structured reports must be returned in one go. Disabling streaming output prevents data corruption from segmented parsing |
data_sync_interval | 3600 seconds | Matches the hourly update rhythm of refinery yield rate reports, avoids frequent calls to unchanged data sources |
tool_call_retry_count | 2 times | Addresses occasional network fluctuations across multi-source APIs, reduces tool calling failure rates |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing.
Three Common Errors
- Numeric parameters are forced to strings when calling MCP services. Phenomenon: Tool return values for fields such as energy consumption and sales price are wrapped in string format, making them unavailable directly for yield rate calculations. Cause: MCP service version 4.9.6 defaults to forcing all request parameters to string types, and does not retain original data types.
- Tool calling returns empty results. Phenomenon: The conversation node triggers the
LLM_model_response_emptyflag, or the tool returns an empty dataset. Cause: No time range parameter is specified for data queries, so the API fails to match refinery operation and market data for the corresponding time period. - Tool calling output terminates early. Phenomenon: The API returns a
408 Request Timeoutstatus code, or tool calling in a conversation is truncated before completion. Cause: Thetool_call_timeoutparameter is not adjusted, and the default duration is insufficient to cover the time required for pulling multi-source data.
How to Confirm Successful Configuration
- Navigate to the FastGPT tool debugging panel, select the configured refinery yield rate tool, manually trigger a call, and check if the returned fields include the preset filter fields and the numerical types meet expectations.
- View the tool calling logs, confirm that the duration of each call does not exceed the configured
tool_call_timeoutparameter value, and the number of retries matches the configuredtool_call_retry_count. - Compare the timestamp of the tool returned dataset with the update rhythm of the data source, confirm that the call interval matches the configured
data_sync_intervalparameter. - Test the multi-tool parallel call scenario, check if data from each data source has completed time alignment, and there are no timing errors.
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.