Tool Calling and Plugins for Construction Machinery Yield

What the data for this category looks like

Data for construction machinery yield rates comes from three sources: real-time operational data reported by device networking terminals, public quotation data from regional rental markets, and regional operational data from industry monitoring institutions. There are three update tiers. Shift-level operating data syncs every hour. Periodic operational data for individual devices updates daily. Monthly cumulative operational data is released three business days after the month ends.

Each data entry includes these fields: unique device identifier, statistical cycle start time, operating duration, rental billing amount, maintenance cost expenditure, and cumulative operational revenue. All fields use standard units: operating duration is measured in hours, while billing amounts, costs, and revenue are measured in yuan.

What constraints these characteristics impose on tool calling and plugins

Multi-source data requires integration with multiple independent plugins. Tool calling must support parallel calls to multiple data sources and result aggregation.

Data with different update frequencies has varying pull frequency needs. Configure differentiated synchronization cycles for each data source to avoid frequently pulling outdated data or missing latest updates.

A single device can have multiple time-series data entries, so tool calling must support precise filtering by unique device identifier to return data for the target device.

Varied fields and units require plugins to include automatic field mapping and unit adaptation capabilities, preventing calculation errors caused by mismatched field names or units.

How to set configurations

Configuration Item	Recommended Value	Rationale
`tool_call_timeout`	25–35 seconds	Adapts to the interface response delay of construction machinery real-time operating data, preventing valid calls from being interrupted by timeouts
`max_tool_invocations_per_turn`	2–4 times	Limits the number of tool calls per dialogue turn, adapting to multi-data-source aggregation scenario requirements
`field_mapping_mode`	Automatic field alignment	Adapts to differences in field names across multiple data sources, such as varying naming rules for device identifiers
`data_sync_cycle`	1 hour	Matches the update frequency of shift-level operating data, avoiding repeated pulls of unchanged historical data
`result_filter_rule`	Filter by device ID	Adapts to filtering requirements for multiple cycle data of a single device, accurately returning data for the target device
`plugin_auth_method`	API key authentication	Adapts to standard authentication methods used by most construction machinery data sources, ensuring data access security

The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.

Three common mistakes

Phenomenon: After enabling the tool calling function, dialogue return results do not include the model's thinking process, only displaying tool call results. Cause: The enable_think_content parameter is not enabled, causing the model to skip the thought chain generation step in tool calling mode.
Phenomenon: Frequent 400 Bad Request errors occur when calling tools. Cause: field_mapping_mode is not configured for automatic alignment, causing passed field names to not meet the requirements of the data source interface.
Phenomenon: 504 Gateway Timeout errors appear when calling tools. Cause: The tool_call_timeout configuration value is smaller than the actual response time of the data source interface, failing to adapt to the normal response delay of the interface.

How to confirm configurations are correct

Initiate a test dialogue that includes a specified device ID. Check if the tool call return results only include data for the target device. Adjust result_filter_rule until the expected outcome is achieved.
View the dialogue log to confirm whether a natural language thinking process is generated during the tool call process. Adjust the enable_think_content parameter until the expected outcome is achieved.
Test multiple calls to the same data source. Check if the update time of the returned data matches the actual update frequency of the data source. Adjust data_sync_cycle until the expected outcome is achieved.
Call the dialogue interface. Check if the return results include metadata information for tool calls. Adjust the return_tool_info parameter until the expected outcome is achieved.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.