What data looks like for this category
Data sources for this category include ERP/WMS systems, barcode or RFID collection devices, procurement and production work orders, warehouse location management modules, AGV transport systems, and weighing equipment. Daily generated documents include receiving slips, putaway slips, material picking and return slips, inventory transfer slips, inventory count sheets, lot ledgers, first-in first-out (FIFO) and expiration date records. Data update frequencies are divided into three categories: real-time (e.g., scanned warehouse entry), daily (e.g., daily inventory reports), and monthly inventory checks (e.g., full warehouse book vs physical stock reconciliation). Single data record fields include material code, lot number, warehouse location number, inbound/outbound quantity, expiration date, inspection status, and associated work order number. Units include pieces, kilograms, location codes, dates, and more.
What constraints these characteristics impose on tool calling and plugin usage
Data sources for this category are scattered, and field requirements are strict. Tool calling must support parallel or sequential pulling of multi-source data. Preconfigured field mapping rules for each data source are required. Strong validation requirements for lots and expiration dates mean plugin calls must specify validation logic for specific fields to avoid errors and omissions. For real-time data update scenarios, tool calling timeout settings must be adapted to short-cycle data pulling to prevent data lag. The core goal of consistent book and physical stock requires ledger data returned by tools to include complete associated relationships, such as binding information between work orders, warehouse locations, and material lots. It also requires support for identifying and extracting abnormal data.
How to configure settings
The following configurations are based on the interface and parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to multi-plugin parallel calling scenarios, automatically selects the tool matching current ledger pulling requirements |
| Workflow/API function-call configuration | Enabled | Forces tools to return structured data, enabling standardized parsing of fields such as lot numbers and expiration dates |
| Tool calling timeout setting | 30–60 seconds | Adapts to real-time and daily data pulling rhythms, avoids long waits that cause ledger data lag |
| Multi-source data source mapping | Configure field correspondence separately for ERP/WMS, barcode collection, and AGV systems | Solves the problem of scattered data sources for this category, ensures field consistency across cross-system data |
| Lot field validation rules | Enable required validation for material code, lot number, and expiration date | Aligns with core business requirements of consistent book and physical stock and lot management for this category |
| Abnormal data extraction switch | Enable identification of book-physical stock differences, stagnant materials, and expired lots | Directly extracts high-risk data from ledgers, simplifies report generation workflows |
Three common configuration mistakes
- A 504 timeout error is returned when calling an external MCP plugin. The cause is failure to adapt to the real-time data pulling rhythm of this category, with an overly short timeout setting that cannot complete batch pulling of multi-source data.
- An "associated data not found" error is returned when calling a work order-bound workflow. The cause is failure to correctly obtain and pass the workflow ID parameter of the corresponding work order, preventing the tool from matching target ledger data.
- A parameter format error is returned when calling the API debugging interface. The cause is failure to pass required parameters such as material code and warehouse location number in accordance with the document field requirements of this category, causing interface validation failure.
How to confirm configuration is complete
- Run a single tool calling test, check if the returned structured data includes core business fields such as material code, lot number, and expiration date.
- Simulate a multi-source data pulling scenario, verify that field mapping between ERP/WMS and barcode collection system data is correctly matched.
- Trigger the abnormal data extraction logic, confirm that target abnormal data such as book-physical stock differences and expired lots can be identified.
- Adjust the tool calling timeout setting, test data pulling results across different durations, confirm alignment with business update rhythms.
The values provided here are common starting points, and should be evaluated against the reader's own samples.