What the data for this category looks like
Data sources for internal office and employee services in railway operations include train dispatching systems, passenger ticketing platforms, equipment inspection terminals, and internal OA attendance modules. Update frequencies are as follows: real-time train data is updated every second, inspection records are uploaded after each shift is completed, and scheduling and attendance data is updated daily. Each individual data entry includes business identifier fields (such as train number, inspection ID), timestamp, operation content, and associated device or personnel ID. Field units include hours, train numbers, four-digit exception codes, and more. The document structure does not contain overly deeply nested content, and some fields have optional null values.
What constraints these characteristics impose on tool calling and plugins
Reference only; verify against the target FastGPT release and deployment documentation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapts to dynamic tool selection requirements across multiple railway operation business scenarios, supports the model to independently determine whether to call tools |
| Single tool call timeout | 300 seconds | Matches the average response duration of businesses such as railway inspection and ticket inquiry, avoids timeout interruptions |
| Tool call result cache duration | 60–120 seconds | Balances the timeliness of real-time train data and interface call frequency, reduces system load |
functionCall | Enabled | Supports the model to return tool call parameters in a preset format, adapts to standardized processing requirements for multiple railway field data |
| Workflow tool trigger threshold | Top 3 matching results | For classification scenarios of railway work orders and inspection records, ensures core information is prioritized for tool calls |
Three Common Mistakes
- Issue: After calling a query tool associated with a knowledge base, no relevant content is returned, and status code 403 is returned. Cause: Access permissions for cross-team knowledge bases are not configured, so the tool call cannot obtain dataset permissions for the target knowledge base.
- Issue: When calling a local MCP tool, the prompt "specified tool not found" is displayed. Cause: The port of the local MCP service is not added to the deployment environment's whitelist, and the correct local service address is not filled in the plugin configuration.
- Issue: Tool calls in workflows are not triggered, and the model directly generates natural language responses. Cause: The
functionCallfunction is not enabled, so the model cannot generate tool call instructions in the preset format.
How to Confirm Proper Configuration
- Initiate a test request that includes a railway train number query, check whether correct train data is returned in the tool call log.
- Call the local MCP tool to test the document typesetting function, confirm that the results returned by the tool match the expected revised format.
- Check the value of the
toolChoiceconfiguration item, confirm that the model automatically triggers the call process when tool calling is required. - Review the permission configuration of the cross-team knowledge base, confirm that the tool call account has access permissions for the target knowledge base.
The values provided here are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.