What data for this category looks like
The data for this category primarily comes from object storage, data warehouses, annotation platforms, and customer acceptance feedback. The update rhythm is once per batch delivery or version release. Daily associated documents include dataset descriptions, version records, delivery lists, and acceptance reports. Common fields include dataset ID, version number, sample count, storage path, acceptance status, and change description. Common units include file count, storage capacity, version iteration cycle, and similar metrics.
What constraints these characteristics impose on tool calling and plugins
Decentralized data sources require tool calling to adapt to multi-platform retrieval logic. Cross-source access traffic splitting rules must be configured. Update rhythm varies with delivery batches, so tools must support on-demand triggering to match non-fixed version update cycles. Associated documents have diverse fields, so tool calling must accurately match fields such as dataset ID and version number to avoid data mixing. The verification process for version numbers and delivery completeness is complex. Tools must include built-in corresponding verification logic to reduce manual verification costs.
How to configure settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | ["datasetDelivery", "changeReview"] | Adapts to dual-scenario traffic splitting for code review and dataset delivery |
| Workflow traffic splitting rules | Split traffic by request interface identifier | Differentiates paths between knowledge base access and MCP calls |
| MCP call timeout setting | 10–15 seconds | Matches verification time consumption for single-batch dataset delivery |
| Multi-source data matching rules | Match by dataset ID + version number | Prevents mixing of data from different dataset batches |
functionCall | Enable field verification mode | Ensures field completeness of delivery lists and acceptance reports |
| Version verification trigger condition | Automatically trigger after each delivery is completed | Matches the update rhythm of each version |
Three common configuration errors
- Phenomenon: MCP calls return empty values in high-concurrency scenarios (2-3 calls per second). Cause: No timeout retry mechanism for MCP calls is configured. Concurrent requests exceed the service's carrying capacity, causing requests to time out without returning.
- Phenomenon: Unable to connect when calling the FastGPT chat interface via Python. Cause: Interface authentication parameters or request header formats are not configured correctly, leading to identity verification failure.
- Phenomenon: Dataset version confusion occurs during tool calling. Cause: Multi-source data matching rules are not configured using dataset ID + version number, leading to mixing of data from different dataset batches.
How to confirm configuration is correct
- Submit a single code change review request, check the tool calling logs to confirm that the traffic splitting rule correctly matches the corresponding scenario.
- Trigger a dataset delivery process, verify whether the MCP call return results include complete delivery list fields.
- Simulate high-concurrency requests to confirm that tool call return results have no empty values or missing fields.
- Submit dataset delivery requests for different versions, confirm that the version verification logic correctly triggers and generates corresponding version records.
The values provided 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.