What this category of data looks like
Data for this category comes primarily from material grade ledgers, customer technical requirement documents, furnace temperature control instruments, furnace temperature uniformity test reports, export data from hardness testers and metallography equipment, and workshop MES systems. Data is updated per furnace load or batch. Core documents include heat treatment process cards, furnace loading layout diagrams, real-time furnace temperature curves, hardness and metallography test reports, equipment inspection records, and exception handling orders. Data fields include material grade, heating temperature, holding time, cooling rate, furnace temperature deviation value, hardness value, metallographic structure type, and more. Some parameters have clear units: temperature in degrees Celsius, duration in minutes. Unstructured data such as furnace temperature curves requires extraction via chart parsing tools.
What constraints do these characteristics impose on tool calling and plugins
High-frequency updates per furnace load require tool calling to support low-latency triggering, to avoid delays that interrupt audit workflows. Multiple types of heterogeneous documents (process cards, curves, reports) require plugins to adapt to different format parsing, and support extraction of both structured fields and unstructured chart data. Data has strong correlation: tool calling must be used to link material grades, customer requirements, and test results. Plugins must support cross-data source field matching. A single exception can lead to full furnace scrap, so tool calling result verification must cover key parameters, and trigger preset exception handling workflows when anomalies occur. Strong data correlation also requires that tool calling context retains all associated data for the current furnace load, to avoid mixing data across batches.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to tool calling needs for multi-type heterogeneous documents, no manual tool specification required, covers multiple audit scenarios including process cards and test reports |
| Tool call timeout duration | 300 seconds | Matches the time required for extraction and verification of data such as furnace temperature curves and metallography reports, to avoid interrupting audit workflows due to timeouts |
| Tool return result verification rules | Verify against required fields (material grade, furnace temperature, hardness value) | Ensure extracted data meets core heat treatment audit requirements, prevent invalid results from entering subsequent processes |
| Tool call failure retry count | 2 times | Balances retry cost and workflow efficiency, addresses temporary device data reading exceptions or network fluctuations |
| Workflow/API function-call configuration | Enabled | Supports structured tool calling parameter transfer, enables accurate transmission of numerical parameters such as temperature and duration |
| Tool call result format | Structured JSON | Facilitates subsequent field matching with exception handling orders in MES systems, simplifies automated processing of audit workflows |
Three common mistakes
- Phenomenon: Tool calling returns empty results with no valid heat treatment process or test data. Cause: Required field verification for tool return results is not configured, causing results that do not match core fields such as material grade or furnace temperature deviation to be discarded.
- Phenomenon: When calling the MCP service, the type of custom parameters is fixed to string. Cause: Structured parameter declaration is not enabled in plugin configuration, causing all incoming parameters to be passed as string format by default, which cannot adapt to numerical parameters such as temperature and duration.
- Phenomenon: Stream output from tool calling cannot be stopped actively, continuously occupying session resources. Cause: The stream output switch for tool calling is not turned off, causing the session to fail to end the audit process normally.
How to confirm the configuration is correct
- Upload a standard heat treatment process card, trigger tool calling, and verify that extracted fields such as material grade and heating temperature match the document content.
- Simulate a furnace temperature data reading exception, trigger tool calling, and verify that retry operations are performed according to the configured retry count.
- Access the plugin configuration page in FastGPT v4.17.0, confirm that the
toolChoiceparameter is set to auto or the specified tool type, which meets current audit scenario requirements. - After turning off the stream output switch for tool calling, trigger tool calling, and verify that the session ends normally after tool calling is completed, with no continuous output.
The values provided here are common starting points, and should be evaluated against the reader's own samples.