What data in this category looks like
Data sources for this category include raw datasets, ETL logs, annotation platform outputs, and sampling results. Data updates occur daily or are triggered per batch task. Daily produced documents include cleaning rules, quality inspection sampling tables, abnormal data lists, and delivery reports. Data fields cover data unique identifiers, original field values, processing batch numbers, abnormal type codes, sampling ratios, delivery verification status, and more. There are no unified mandatory units, and some fields must match the unit definitions of upstream data sources.
What constraints these characteristics impose on the "tool calling and plugins" workflow
Multi-source and batched data input requires tool calling to support triggering per task batch to avoid mixing data across batches. Daily or batch-level update rhythms require tool calling timeout thresholds to adapt to the execution duration of single-batch tasks. Diverse document formats and field structures require plugins to be compatible with parsing non-standard documents such as cleaning rules and abnormal lists. Fields without unified units require that the unit association logic of original fields is retained during tool calling to prevent data unit distortion.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
functionCall | Mandatory enablement | Adapts to the structured rule calling requirements of data cleaning and quality inspection, and complies with FastGPT v4.17.0 tool calling parameter specifications |
toolChoice | Specified as auto | Allows the model to independently choose whether to call tools, adapting to flexible quality inspection scenarios |
| Maximum context token count | 8000–12000 token | Adapts to the context carrying requirements of multiple documents such as cleaning rules and abnormal lists |
| Single tool call limit | First 3 abnormal data entries | Matches the conventional abnormal volume of daily quality inspection sampling, preventing overload in a single call |
| Timeout threshold | 600 seconds | Adapts to the conventional execution duration of single-batch data cleaning and quality inspection tasks |
| Trigger method | Trigger per task batch | Matches the batch-update data rhythm of this category, avoiding mixing data across batches |
Three common configuration mistakes
- Phenomenon: MCP service call fails, returning connection timeout or permission denied errors. Cause: The MCP proxy endpoint is not configured correctly, and data source access rules for data cleaning tasks are not matched, causing the tool to fail to call the corresponding service.
- Phenomenon: The cleaning rule parsing result output by the model has unit distortion. Cause: The unit association logic of original fields is not retained, and field units are directly converted during tool calling, resulting in distorted data indicators.
- Phenomenon: A context overflow error is triggered during tool calling. Cause: The maximum context token count is set too small, failing to adapt to the context carrying requirements of multiple documents such as cleaning rules and abnormal lists, exceeding the context length supported by the model.
How to confirm the configuration is correct
- Submit single-batch test data, check the tool calling logs, and confirm that the trigger rule matches the configured trigger method.
- Check the tool calling parameters output by the model, confirm that the calling mode complies with configuration requirements, and there are no format errors.
- Verify abnormal data processing results, confirm that unit association logic is retained, and there are no unit distortion issues.
- Simulate a long-context test scenario, confirm that no context overflow error is triggered, and adjust the context threshold according to actual tasks.
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.