What the data for this category looks like
Data for this category primarily comes from database monitoring systems, operation audit logs, SQL execution plan files, and internal ticketing systems. Daily generated documents include database change orders, slow SQL performance reports, backup and recovery records, and permission application approval forms. Data updates are triggered by operations: corresponding documents are generated after a single database change, and a global performance report is generated through daily aggregation. Data fields include instance ID, execution duration, permission scope, change version number, and others. There is no unified fixed multi-dimensional statistical unit; records are only stored separately by database instance and operation dimensions.
What constraints these characteristics impose on the tool calling and plugins workflow
Decentralized data sources require tool calling to adapt to multiple sets of authentication rules and interface formats. Separate configuration of calling authentication and interface parameters is needed for database monitoring, operation audit logs, and internal ticketing systems. Diverse document structures require plugins to support parsing business files in different formats. Field mapping rules must be defined separately for change orders and slow SQL reports. Unfixed update frequencies require plugins to support both on-demand triggering and scheduled pulling modes, to avoid redundant calls or data lag. Database operation and maintenance errors have serious consequences. Tool calling must be configured with a failure retry mechanism and exception alarm triggering logic.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | "auto" or a specified tool list | Let the model automatically match the database operation and maintenance tools required for the current task, or fix calls to preset slow SQL analysis and permission verification tools |
functionCall | Pass the corresponding parameter object according to the definition of the preset tool | Clearly specify the target tool and its parameters to ensure the accuracy of tool calling |
| Tool triggering timing | Triggered upon change order submission / scheduled daily | Align with the update rhythm of database operation and maintenance data, trigger tool calling on demand or periodically aggregate data |
| Number of failure retries | 2–3 times | Balance call success rate and resource consumption, avoid invalid retries |
| Exception alarm threshold | Tool call failure rate exceeds 10% | Trigger operation and maintenance alarms to promptly troubleshoot tool calling exceptions |
| Plugin permission scope | Only bind read-only permissions for the corresponding database instance | Follow the principle of least privilege for database operation and maintenance, prevent unauthorized operations |
This configuration applies to FastGPT v4.17.0
Three common mistakes
- A 500 status code is returned during tool calling, and logs show document parsing failure. This occurs because the custom field format of the database change order is not adapted, preventing the plugin from correctly extracting the instance ID and change content.
- Parameters are parsed normally after tool calling, but the corresponding operation is not executed. This occurs because the database instance authentication information for the tool is not configured, or the authentication information has expired, leading to rejected calls.
- A permission error is prompted when calling a locally deployed embedding model. This occurs because the calling port and access whitelist for the local model are not correctly configured, preventing FastGPT from connecting to the deployed model service properly.
How to confirm the configuration is complete
- Manually trigger tool calling, and check if the returned result includes expected slow SQL analysis data or permission verification results.
- View tool calling logs to confirm there are no abnormalities in authentication information, parameter transmission, or interface returns.
- Simulate a database change operation to verify if the plugin can automatically trigger and generate corresponding test cases and defect analysis results.
- Check the exception alarm configuration, simulate a tool call failure, and confirm the alarm triggers normally.
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 2026-10-19.