What the Data for This Category Looks Like
Quality traceability data for metal cutting machining comes from product drawings and 3D models, CAPP/CAM systems, machine tool collection terminals, tool management systems, MES systems, and measuring and inspection equipment. Daily documents include process routes, operation cards, tool lists, CNC programs, first-piece inspection records, patrol inspection forms, and measuring tool calibration records. Fields cover process numbers, tool models, cutting parameters, inspection dimensions, deviation values, and more. Units are mostly millimeters, revolutions per minute, and millimeters per revolution. Data updates trigger per work order, per workpiece, at sampling inspection nodes, or when tools are replaced.
Constraints on Tool Calling and Plugins
Data is scattered across multiple heterogeneous systems. Tool calling must support linked queries across cross-system data sources, to avoid retrieving only single-module data in a single call. Data updates occur frequently with unpredictable trigger nodes. Tool calling must support on-demand real-time pulling of the latest data to ensure traceability result timeliness. Document formats are diverse, including structured tables, G-code programs, inspection reports, and more. Tools must have parsing capabilities for corresponding formats to accurately extract key fields. The parameter precision required for quality problem correlation is high. Tool call return results must strictly match the field structure of operation cards and tool lists, to avoid analysis deviations caused by parameter misalignment.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapt to dynamic calling requirements for multiple data sources in metal cutting machining, automatically select the corresponding tool based on user questions |
| Tool call timeout period | 300–600 seconds | Machine tool data pulling, CNC program parsing, and cross-system data linkage require long processing cycles |
| Maximum number of results returned per tool call | Top 8 entries | Avoid excessive data causing context overload, while covering main quality-related parameters |
| Workflow/API function-call configuration | Forced tool call triggering | Meet the need to obtain real-time latest inspection and tool data for quality traceability scenarios, ensure calling accuracy |
| CNC program parsing cache time | 24 hours | CNC program change frequency is low, 24-hour cache balances real-time performance and system load |
| Token limit for thinking process output | 10240 token | Ensure complete display of thinking process content while avoiding context overload |
| Tool result rendering rules | Bind conversation display format by field mapping | Match structured fields of operation cards and tool lists, ensure returned data can be directly used for defect analysis |
Three Common Mistakes
- Phenomenon: The image URL returned by tool calling cannot be displayed normally in the conversation interface. Cause: No media type parsing rule for tool results is configured, and the image link field is not bound to the image tag of conversation rendering, so only plain text links are returned.
- Phenomenon: Local MCP service call fails, returning connection timeout or address unreachable. Cause: No access whitelist for local services is added in FastGPT plugin configuration, and the correct service listening port is not specified, so connection cannot be established.
- Phenomenon: No thinking process is output after tool calling is completed. Cause:
functionCallis set to forced triggering mode, and the output configuration for thinking process is not retained, so the model only returns tool call results and does not show the analysis and derivation process.
How to Verify Correct Configuration
- Submit a test request containing "query current tool wear status for the current process", check whether the tool call returns the latest wear data from the tool management system.
- Upload a test work order containing inspection image URLs, check whether the corresponding image is rendered normally in the conversation interface.
- Call the interface of the locally deployed MCP service for testing, check whether the returned results match the preset process parameters and inspection data.
- View the tool call logs in FastGPT v4.17.0, confirm that the configurations of
toolChoiceandfunctionCallare triggered as expected, and there are no abnormal errors.
The values listed above are common starting points, and should be evaluated against the reader's own samples.