What the Data for This Category Looks Like
Customer requirement pool management data comes from ticketing systems, customer interview records, CRM systems, and product roadmap documents. Daily maintained documents include requirement pool lists, requirement evaluation forms, customer feedback summaries, and version planning descriptions. Data update frequency is weekly or per version release cycle. Fields include requirement source, associated customer, priority tag, scheduling status, and merge marker. Data is stored as structured lists. Each single record contains clear business attributes and associated information, with no redundant nested structures.
What Constraints These Characteristics Impose on Tool Calling and Plugins
Because data sources are scattered and update rhythms are fixed, tool calling must support pulling and integrating data from multiple systems, and avoid single-call timeouts. Requirement pool data is stored as lists. Plugins must adapt to batch processing logic, and support field-based filtering and merging. Duplicate requirement merging is a core time-consuming step. Tool calling must include built-in similarity matching logic to reduce manual operations. At the same time, code change reviews must link to the latest requirement pool data. Plugin trigger timing must match the update rhythm, to avoid using outdated requirement information that affects review results.
The following configuration applies to FastGPT v4.17.0.
How to Set the Configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Tool Calling Mode | Batch Pull Mode | Adapts to the batch data structure of requirement pool lists, supports pulling multiple requirement records at one time |
toolChoice | auto | Automatically matches requirement pool data for the current workflow, no need to manually specify the calling scope |
functionCall | enabled | Enables function calling capabilities, supports merging and sorting pulled requirement data |
| Maximum Number of Entries per Request | First 10 entries | Matches the standard number of entries displayed per page of the requirement pool, avoids excessive data volume in a single request causing timeouts |
| Timeout Threshold | 600 seconds | Adapts to the average data synchronization time of CRM and ticketing systems, avoids call failures due to network latency |
| Plugin Trigger Timing | Within 30 minutes after requirement pool update | Matches the data update frequency, ensures the latest requirement pool data is used during the review process |
Three Common Misconfigurations
- A
400 InternalError.Algo.InvalidParametererror is returned when calling the tool. The cause is that thetoolChoiceparameter is not configured correctly, causing the tool calling parameter format to not meet system requirements. - Plugins created in the production environment cannot be triggered in the code change review workflow. The cause is that the plugin is not bound to the corresponding workflow node for requirement pool management, and only basic configuration is completed in the backend.
- The number of requirement merging results returned by the plugin is less than expected. The cause is that the maximum number of entries per request is set too small, causing some requirement records to not be fully pulled.
How to Confirm the Configuration Is Correct
- Log in to the plugin management page in the FastGPT backend, check the save status of the corresponding configuration items, confirm that all parameters have been stored correctly and there are no abnormal prompts.
- Manually trigger a requirement pool update event, check whether the plugin automatically executes and returns expected requirement merging and sorting results.
- Enter the code change review workflow, call the configured plugin, verify that it can correctly pull the latest data from the current requirement pool and link it to the review content.
- Check the system operation logs, confirm that the tool calling parameter format meets the requirements, and no error messages are returned.
The values listed above are common starting points. Adjust them based on your own specific samples and use cases.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.