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What data for this category consists of
Data for this category mainly comes from cloud bills, monitoring systems, business forecasts, and stress test reports. Most updates occur weekly or monthly. Daily produced documents include capacity planning reports, scaling applications, resource utilization reports, and budget calculation sheets. Fields cover resource usage time periods, node specifications, call volume peaks, budget limits, and similar items. There are no fixed unified unit standards. Matching measurement methods must align with the data source type. Data structures adjust based on business scenarios, with no mandatory unified format.
What constraints these characteristics impose on the tool calling and plugins workflow
Since data sources are scattered and update cycles are mostly weekly or monthly, tool calling must support connecting multiple types of data source plugins. This avoids repetitive manual data aggregation work. Document structures have no fixed mandatory format. Plugins must support field mapping configuration to adapt to document template requirements of different businesses. Peak prediction and resource bottleneck identification take extended time. Tool calling must support batch pulling historical data and parallel computing capabilities. This reduces time costs from manual intervention. At the same time, the budget and procurement coordination link requires accurate resource data. The output of tool calling must comply with field specifications for operation and maintenance and financial documents. This prevents process blockages caused by misplaced data.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Allows the system to automatically select tools matching capacity planning scenarios, no manual specification required |
| Data source plugin scheduling interval | 7 days | Matches the weekly update frequency of resource utilization reports, avoids pulling redundant data repeatedly |
functionCall | Enabled | Allows calling preset dedicated functions for capacity calculation and peak prediction, improves process automation |
| Function call timeout threshold | 300 seconds | Addresses delays from cross-cloud monitoring data pulling, prevents process interruptions due to timeouts |
| Result output format | JSON format | Adapts to field parsing needs of various capacity planning documents, simplifies subsequent format conversion |
| Plugin dependency verification switch | Enabled | Pre-checks the availability of data source plugins and authentication parameters, reduces runtime errors |
Three common mistakes
- Nested workflow calls to capacity planning tools fail to generate complete documents. The cause is failure to map data source fields to required items in document templates, leading to missing key fields.
- Calling third-party cloud bill interfaces returns the
Error: write EPROTerror. The cause is failure to configure network proxy rules for the plugin, leading to blocked cross-region access. - Calling the Qwen model for peak prediction fails to execute normally. The cause is failure to enable the
functionCallconfiguration, preventing the model from calling preset prediction functions.
How to confirm configurations are properly set
- Trigger the tool calling workflow. Check if the pulled data source data includes preset resource usage time period and node specification fields. Confirm field matching degree per business document requirements.
- View plugin running logs. Confirm there are no
write EPROTnetwork errors. Confirm network connectivity per access rules of the target data source. - Trigger the model calling step. Check if intermediate results matching the capacity planning report format are generated. Confirm output format per preset template requirements.
- Test nested workflow calls. Confirm the sub-workflow can fully execute preset resource calculation logic. Confirm workflow integrity per workflow node configuration.
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.