What this category of data looks like
This category covers multi-source scattered data, including customer drawings and special characteristic lists, ERP/MES systems, measuring tools, equipment process parameters, and supplier quality certificates. Daily associated documents include control plans, process flowcharts, work instructions, first article and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Data updates occur per batch, per shift, and via continuous collection. Fields include batch number, process number, test values, special characteristic codes, supplier codes, and more. Units include length, pressure, torque, and other automotive component-specific measurement standards.
Constraints for Tool Calling and Plugins
Multi-source scattered data sources require tool calling to support plugins that connect to multiple heterogeneous systems. This avoids fragmented data pulling. High-frequency updated batch and real-time data require tools to adapt to near-real-time pulling logic. Static offline indexes cannot be relied on here. Diverse document formats and structured fields require plugins to parse multiple document types such as tables and reports. Core metadata such as special characteristics and batch numbers must be retained. Quality systems with strong constraints from vehicle customers require tool calling results to retain complete traceability links. Key information associated with customer special characteristics and suppliers cannot be lost.
Configuration Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | Auto | Adapts to dynamic tool selection when calling multi-source data, avoids missing heterogeneous data source scenarios with fixed tools |
| Workflow/API function-call configuration | Enabled | Supports structured output of standardized fields required for 8D reports and defect analysis, meets compliance requirements for automotive component quality documents |
| Maximum single tool call count | Top 3 | Adapts to the batch data scale of high-volume manufacturing, prevents resource overload from calling too many data sources simultaneously |
| Tool call timeout period | 60–120 seconds | Matches average pulling delays of ERP/MES and measuring tool data, prevents task failure due to slow data collection |
| Result grouping rule | Group by batch | Aligns with core requirements of automotive component quality traceability, facilitates quick location of associated data for defective items in the same batch |
Common Mistakes
- Symptom: Unable to add a locally running MCP service to the tool set. Cause: Failed to correctly fill in the communication port and address of the MCP service on the FastGPT plugin management page, and did not follow the tool registration process.
- Symptom: Tool call returns empty results. Cause: MCP plugins connected to MES and measuring tools are not configured, resulting in inability to pull real-time test data.
- Symptom: Call timeout returns 504 status code. Cause: The set timeout period is shorter than the average pulling delay of measuring tool data, and does not match the data collection rhythm of high-volume manufacturing.
How to Confirm Proper Configuration
- Manually trigger a quality query for a specified batch, verify that SPC reports and equipment process parameter data for the corresponding batch can be pulled, to confirm data source connections are normal.
- Submit a query containing special characteristic parameters, verify that
functionCallcan output structured defect analysis fields, to confirm the structured output rule is active. - View tool call logs, confirm that the timeout setting matches the actual delay of data pulling, with no frequent timeout errors.
- Check the plugin management page, confirm that the MCP services corresponding to MES and measuring tools have been added, and the communication address and port configuration are correct.
The values provided are common starting points and should be measured against the reader's own samples.