What Data for This Category Looks Like
Data sources for this category include Gerber files, drill files, BOM lists provided by customers, as well as engineering documents generated by CAM, production equipment parameters, tank solution test results, AOI inspection reports, electrical testing reports and slice analysis records. Data is updated in real time or in batches according to production batches and team shifts. Single-batch production data includes fields such as drilling rotation speed, lamination temperature, etching compensation value, with units including revolutions per minute, degrees Celsius, millimeters, and others. The document structure is split by production processes, covering the full workflow from data conversion to finished product inspection.
What Constraints These Characteristics Impose on the Tool Calling and Plugins Link
Data sources for this category are scattered and updated frequently. Tool calling must support connecting to multiple data source interfaces simultaneously to avoid incomplete information caused by calling only a single data source at a time. Documents are split by production process, so tools must support accurately matching corresponding documents using process keywords to avoid cross-process information confusion. Fields include parameters with specific units, so tool calling must include built-in unit conversion logic to ensure incoming parameters match production equipment requirements. Data errors can lead to full batch scrapping, so tools must add parameter validity verification links to intercept abnormal inputs in advance.
Configuration Guidelines
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapt to parallel multi-tool calling scenarios, automatically select the tool matching the current question |
| Tool call timeout | 300 seconds | Cover the entire process of pulling and parsing single-batch production data, avoid timeouts caused by large data volumes |
| Maximum number of retrieval results | Top 8 entries | The number of documents related to a single process is concentrated, too many results will exceed the context carrying capacity |
| Workflow/API function-call configuration | Bind two types of tools: process parameter query and document retrieval | Cover core requirements of process specification query and real-time production parameter verification |
| Context window length | 8000–12000 characters | The total character count of single-batch production data is concentrated in this range, calibrated based on actual measurements |
Three Common Misconfigurations
- Symptom: The interface prompts failed to connect to the MCP service, and logs show connection timeout. Cause: The local MCP service address started via npx was not added to the FastGPT toolset whitelist, and the communication port was not open.
- Symptom: Tool call returns empty results with no error message. Cause: Retrieval was not performed using production process keywords, and irrelevant document content was matched.
- Symptom: Tool activation status shows "Temporary" and cannot be called continuously. Cause: No long-term valid MCP service authentication credentials were configured, only temporarily generated session keys were used.
How to Confirm Proper Configuration
- Initiate a query containing specific process parameters, check whether the tool automatically calls the corresponding data retrieval tool.
- View the tool call log to confirm that the pulled data source matches the documents of the current production batch.
- Verify that the parameter units returned by the tool match the units required by production equipment.
- Check the tool activation status to confirm that the temporary activation indicator is no longer displayed.
The values provided are common starting points and should be measured against the reader's own samples.