This document applies to FastGPT v4.17.0.
What data for this category looks like
Data sources for this category include instrumentation systems, internal enterprise data warehouses, customer relationship management systems, and ticket systems. Data update frequency is daily or weekly. Daily produced documents include usage analysis reports, instrumentation plans, indicator caliber tables, and dashboard requirement sheets. Common data fields include customer unique identifiers, function module names, operation frequencies, access durations, ticket request types, and more. There are no uniform mandatory units; some indicators require corresponding units defined based on business caliber.
What constraints these characteristics impose on the "tool calls and plugins" workflow
Dispersed data sources require tool calls to adapt to authentication rules and data return formats of multiple systems, increasing adaptation costs for plugin development. Differences in daily or weekly data update frequencies require plugins to support on-demand data pulling, avoiding high-frequency calls that consume system resources. The diversity of daily documents means plugins must support multiple input formats such as instrumentation plans and indicator caliber tables, and cannot rely on fixed field templates. Non-standardized indicator calibers require tool calls to transmit corresponding caliber descriptions synchronously, preventing deviations in indicator interpretation across links. Customer-associated data must be aggregated by customer dimension, requiring plugins to support data processing grouped by customer ID to meet analysis needs for customer success operations.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapt to multi-source data pulling scenarios, automatically matching corresponding tools to complete data collection and processing |
| Data Pulling Timeout Threshold | 600 seconds | Adapt to interface response durations of most internal enterprise systems, avoiding timeout interruptions to the data pulling process |
| Plugin Input Format Support | JSON, CSV, Excel templates | Cover common formats of daily documents such as instrumentation plans and indicator caliber tables |
functionCall | Enable required field validation | Force validation of core fields such as customer ID and function module, preventing missing key information in analysis results |
| Data Aggregation Cycle | Optional by day or by week | Match the daily or weekly data update frequency, adapting to cycle requirements of different analysis scenarios |
| Plugin Log Level | Only record error logs in production mode | Balance troubleshooting needs and system operating performance, avoiding excessive storage resource consumption by logs |
Three common misconfigurations
- Symptom: After adding a custom plugin to a workflow, no input or output parameters are displayed, and no error logs are generated. Cause: Parameter types and required attributes were not correctly declared in the plugin configuration, leading to front-end rendering exceptions.
- Symptom: The number of results returned after calling a tool does not match expectations, and some customer data is not aggregated. Cause: Grouping rules were not configured based on customer ID, resulting in mixed cross-customer data that cannot meet analysis requirements.
- Symptom: Tool calls time out, with obvious response delays. Cause: The set timeout threshold is too short and does not adapt to the combined response duration of multi-system interfaces, or no retry mechanism was configured to handle temporary network fluctuations.
How to confirm configurations are correct
- Trigger a single tool call, check if the returned raw data includes all expected fields, and verify that field names match the business caliber table.
- Simulate data pulling for different update cycles, confirm that the plugin can trigger data synchronization according to the configured daily or weekly frequency.
- Upload documents such as instrumentation plans and indicator caliber tables, confirm that the plugin can correctly parse and adapt to the input format.
- Check plugin logs, confirm that there are no error messages, and that core field validation rules are working properly.
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.