What data looks like for this category
Data for heat treatment scenarios comes from material grade archives, customer technical specification documents, temperature control instruments, furnace temperature uniformity test reports, export results from hardness testers and metallographic equipment, and workshop MES systems. Data update frequency is per heat load and per batch. Daily generated documents include heat treatment process cards, charging diagrams, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling sheets. Data fields include furnace temperature (unit: ℃), holding duration (unit: minutes or hours), hardness values (unit: HV or HRC), metallographic structure ratings, and other related fields. No additional fictional statistical figures are included.
What constraints these characteristics impose on the "tool calling and plugins" link
Heat treatment scenario data has scattered sources, covering direct device-connected data, archived system documents, and manually filled records. Tool calling must support format adaptation for multi-source data. Data updates occur frequently, with new process and inspection data generated for each heat load. Tool calling must support real-time triggering and dynamic parameter adaptation. Documents in the scenario include structured tables and unstructured charts. Tool calling must support data extraction and filling across different formats. Exception analysis must strictly match process parameters and inspection results. Tool calling result verification logic must cover field integrity and compliance requirements.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapt to dynamic tool calling needs such as process verification and exception analysis in heat treatment scenarios |
| Workflow/API function-call configuration | Trigger according to preset tool parameter format | Must strictly match field requirements of tools including heat treatment process cards and furnace temperature curves |
| Tool call timeout period | 300–600 seconds | Longer waiting cycles are required for heat treatment furnace temperature data collection and metallographic analysis result acquisition |
| Database connection pool size | 8–12 connections | Adapt to concurrent data query and document generation needs for multiple heat loads |
| Tool result verification rules | Match required fields of heat treatment documents | Ensure core information such as charging position and furnace temperature parameters is not missing |
| MCP bound data source scope | Only open workshop-exclusive heat treatment databases | Avoid interference from irrelevant data on tool calling accuracy |
Three common mistakes
- Adding a MySQL database tool call returns a 400 status code (no body). The cause is failure to match and verify tool parameters with fields such as material grade and furnace temperature parameters in heat treatment process cards.
- Custom code calls return AggregateError Code: ETIMEDOUT. The cause is that the tool call timeout threshold is set shorter than the actual time required for furnace temperature curve collection and hardness data reading.
- Deploying MCP without binding exclusive heat treatment data source permissions results in failure to read furnace temperature uniformity test data and process data from the MES system.
How to confirm the configuration is complete
- Initiate a simulated single heat load process query, check whether tool calling can automatically pull furnace temperature curve data corresponding to the heat number.
- Submit an exception report drafting request, verify whether the tool can automatically fill in inspection result fields exported from hardness testers and metallographic equipment.
- View the tool calling logs of FastGPT v4.17.0, confirm that database connections have no timeouts and parameter formats comply with preset specifications.
- Test concurrent calls for multiple heat loads, check whether concurrent processing of tool calls matches the configured connection pool size.
The values given are common starting points and should be measured against the reader's own samples.