What Data for This Category Looks Like
Data for this category primarily originates from tracking systems, data warehouses, CRM systems, and ticket systems. Most updates run on a daily or weekly schedule. Daily associated documents include usage analysis reports, tracking plans, indicator caliber tables, and dashboard requirement forms. Common data fields include customer identifiers, feature access frequency, operation duration, ticket association IDs, and more. Units include counts, minutes, numeric identifiers, and others. The overall structure centers around customer behavior and business metrics.
What Constraints These Characteristics Impose on the "Tool Calling and Plugins" Workflow
Because data sources are scattered, tool calling must adapt to format differences across multiple data sources, and cross-source connection parameters must be configured in advance. The daily/weekly update frequency requires tool calling to match the scheduling rhythm, to avoid frequently pulling incomplete incremental data. The requirement for unified indicator calibers means that caliber verification parameters must be included when calling tools, to prevent generation of analysis results that do not comply with business rules. The need for multi-document association requires plugins to support batch reading of different types of business documents, while also handling compatibility issues with field mapping.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapts to all types of tool calling scenarios, no need to manually specify a single function |
functionCall | [{"name":"get_usage_data","parameters":{"customer_id":"","time_range":""}}] | Explicitly specifies the data analysis tool function to call, prevents the model from generating invalid calls |
| Plugin Data Source Whitelist | Tracking systems, data warehouses, CRM | Matches the data source scope of this category, prevents calling unauthorized external data sources |
| Scheduling Trigger Cycle | 2:00 AM daily or 1:00 AM every Monday | Matches the daily/weekly update frequency of this category's data, avoids pulling incomplete incremental data |
| Single Call Data Volume Limit | 10000 entries | Avoids pulling datasets that exceed the plugin's processing capacity in a single call |
| Call Timeout Duration | 600 seconds | Matches the average time required to pull this category's data, prevents tasks from being interrupted unexpectedly |
This configuration applies to FastGPT v4.17.0.
Three Common Mistakes
- The symptom is that tool calling fails to trigger, with a prompt indicating the current model's tool calling format is not supported. The cause is failing to confirm whether the model supports
functionCallformat calls. Model tool calling compatibility must be verified in advance. - The symptom is unstable tool calling results, with empty return fields or inconsistent formats. The cause is failing to configure parameter verification rules based on this category's indicator caliber table, leading to call parameters generated by the model that do not meet the data source's field requirements.
- The symptom is that specified reference content cannot be obtained after calling an online tool, with empty or irrelevant return results. The cause is failing to bind permissions for the corresponding online tool in
toolChoice, or failing to configure keyword filtering rules for content crawling.
How to Confirm Proper Configuration
- Manually trigger a tool call, check whether the return result includes core fields of this category's data, such as customer identifiers, feature access frequency, and more.
- View the plugin's running logs, confirm that the data source connection is normal, and no permission error or format conversion failure prompts appear.
- Compare the scheduling task's execution results with the manually pulled dataset, confirm that the data update frequency meets the configured cycle requirements.
- Call the abnormal data filtering function, verify that it can correctly identify and filter meaningless empty data batches.
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 2026-10-19.