What the data for this use case looks like
Business data for state-owned asset management companies comes primarily from state-owned asset supervision platforms, internal business ledgers, property rights registration archives, and full-process records of asset disposal. Data update rhythms fall into three categories: asset ledgers are synchronized and updated monthly, property right change information is pushed in real time alongside approval nodes, and disposal progress data is updated weekly. Individual business documents use structured formats, containing fixed fields including unique asset ID, book value (unit: 10,000 yuan), storage location, ownership status, processing node, and handler information, with no redundant unstructured content.
What constraints these characteristics impose on the tool calling and plugins workflow
The prevalence of structured business data requires tool calling plugins to strictly match preset fields, avoiding additional overhead from unstructured text parsing. Real-time updated disposal progress data requires tool calling response durations to align with the approval cycle of business nodes, preventing data desynchronization caused by timeouts. Monthly updated asset ledgers require plugin local cache cycles to match the data update rhythm, preventing calls to expired data. The unique asset ID as a core identifier requires the plugin parameter verification step to enforce checks on the format and existence of this field, ensuring accurate data location.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Tool calling mode | functionCall | Business data uses structured fields, requiring precise function calls to match preset parameters |
| Tool selection strategy | toolChoice | Must specify a calling strategy that matches structured fields, avoiding generalized calls |
| Plugin timeout duration | 30–60 seconds | Disposal progress data updates in real time; excessive timeout will cause calls to use outdated data |
| Parameter verification switch | Enabled | Must enforce verification of unique identifier fields such as asset IDs, preventing calls to invalid data |
| Cache update cycle | Monthly | Asset ledgers are updated monthly; matching the data update rhythm reduces calls to expired data |
| Number of returned entries per single call | Top 3 entries | Core data entries for individual business processing scenarios are concentrated; excessive entries increase context processing load |
Three common misconfigurations
- Phenomenon: An internal server error prompt pops up when installing a database connection plugin. Cause: The access whitelist for the target database is not configured, or the database driver relied on by the plugin does not match the FastGPT runtime environment version.
- Phenomenon: Business attachment data cannot be stored or read during tool calling. Cause: Access keys, bucket permissions, or access addresses for MinIO storage are not correctly configured, preventing the plugin from calling the storage service normally.
- Phenomenon: No dataId is returned after calling a tool via the API. Cause: Core identifier fields such as asset IDs are not passed in the tool calling parameters, so the system cannot generate the corresponding data ID.
How to confirm configurations are correct
- Execute a test tool call, pass an asset ID in the correct format, and verify that the returned data fields match the business ledger.
- Check the plugin running logs to confirm there are no records of parameter verification failures, timeouts, or storage connection errors.
- Modify the cache update cycle to a non-matching value, verify if the tool call results return expired data, then restore the original setting after confirming the cycle configuration is correct.
- Call the API interface to initiate a tool call, check if the returned result contains a valid dataId field.
The values provided are common starting points and should be evaluated against relevant operational 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.