What the Data for This Category Looks Like
The data supporting railway operations service inquiries and responses is sourced primarily from official railway knowledge bases, railway customer service ticket systems, station segment service manuals, and real-time operation interfaces. Data updates are split into two categories: real-time updates including ticket changes and delay information, and daily updates such as new service guides and regulatory announcements. Documents are grouped by scenario into modules like ticket purchasing, ticket refund and rescheduling, security inspection, delay query, and more. Fields include scenario classification, question keywords, standard answers, applicable train number range, and effective date. Train numbers use a combination of digits and letters, and effective dates follow the YYYY-MM-DD format.
Constraints Imposed on Tool Calling and Plugins
The scenario classification and applicable train number range of the data require that corresponding scenario and train number parameters must be passed during tool calling. Valid data cannot be matched without these parameters. Real-time data must be pulled at regular intervals via plugins. Static caching cannot be used, as this will return expired information. The effective date field must be used as a filter condition to avoid returning expired service rules. Most railway service inquiry questions are tied to specific trains or time periods. Tool calling must support dynamic parameter passing to ensure returned answers match user requests.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
toolChoice | auto | Adapt to multi-scenario service inquiry needs, automatically trigger the corresponding railway service tool |
| Tool call timeout duration | 60 seconds | The update delay of real-time railway data such as ticket and delay information is usually within 30 seconds. A timeout will trigger a fallback prompt |
| Knowledge base recall limit | Top 3 entries | Railway service inquiry scenarios are relatively concentrated. Excessive recall will cause context overload |
functionCall | enabled | Explicitly call preset railway service tools to avoid the model calling general knowledge base content |
| Data cache duration | 30 minutes | Match the update frequency of real-time railway ticket data to ensure the timeliness of returned data |
| Tool call retry count | 2 times | Address occasional temporary fluctuations in railway interfaces and improve call success rate |
Three Common Misconfigurations
- Phenomenon: After executing a tool call in a workflow, business fields in the returned result are empty, and core information such as ticket and delay data cannot be extracted. Cause: Required train number and scenario type fields are not configured in the tool call parameters, preventing the tool from matching corresponding railway operation data.
- Phenomenon: After submitting a service inquiry request, the model returns an answer that does not match the current question. For example, querying security inspection requirements but receiving ticket refund rules. Cause: The tool bound to the railway service scenario is not specified via
toolChoice, and the model mistakenly calls general knowledge base content. - Phenomenon: A locally deployed model returns a connection timeout or permission error when calling the MCP service. Cause: The local deployment environment is not configured with the correct MCP service access address and permissions, and cannot connect to the background data interface of railway operations.
How to Verify Correct Configuration
- Submit a service inquiry request that includes a clear train number and specific scenario, and check whether the train number and scenario type parameters are correctly passed in the tool call logs.
- Trigger a tool call and verify that the returned result includes railway operation data corresponding to the scenario, such as specific details like delay duration and ticket refund rules.
- Test the locally deployed model calling the MCP service, and confirm that the returned fields match the results of online model calls.
- Simulate a tool call timeout scenario, and check whether the preset fallback prompt strategy is triggered.
The values given 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.