Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data related to construction machinery business processing mainly comes from equipment operation sensors, offline maintenance work order systems, and equipment management platforms. Sensor data is updated in real time. Maintenance work order data is synced and archived daily. Data documents include fields such as unique device identifiers, working condition monitoring parameters (such as rotational speed, hydraulic oil temperature), work order numbers, processing cycles, and replacement spare part models. Most working condition parameters have physical units. Most work order-related fields are associated with device IDs and operation staff identifiers.
Constraints imposed by these characteristics on tool calling and plugins
The high-frequency updates of real-time sensor data require reasonable request sizing for tool calling, to avoid triggering rate limits or timeouts. The presence of physical units on working condition parameters requires built-in unit conversion logic in plugins, to ensure cross-device parameter comparison is feasible. Device ID is a core associated field. The exact device identifier must be passed during tool calling, otherwise corresponding business data cannot be returned. Parameter fields vary across different construction machinery models. Plugins must support dynamic matching of device-specific fields, to avoid field missing errors.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Tool Calling Mode | functionCall | Enforce parameter passing in the preset plugin function format, to support structured transfer of multi-field parameters for construction machinery |
| Single Request Data Limit | 1000–2000 characters | Match the reasonable scale of single transmission of sensor data, to avoid request timeouts |
| Unit Conversion Switch | Enabled | Adapt to physical unit differences in construction machinery working condition parameters, unify plugin processing logic |
| Request Timeout | 60 seconds | Cover the standard response duration for maintenance work order queries and device data retrieval |
| Plugin Field Mapping | Calibrated based on actual testing | Adapt to parameter field differences across different models, avoid field mismatches caused by hardcoding |
Three common mistakes
- Symptom: No valid response when passing multiple device inspection images during tool calling, and the interface shows missing parameters. Cause: The image paths were not passed in the preset array format required by the plugin. Only passing a single path caused the parameter structure to not meet plugin requirements.
- Symptom: MCP function calls to construction machinery maintenance work order plugins frequently fail, with parameter verification errors returned in logs. Cause: The exact device ID was not passed as a query condition, causing the plugin to fail to match corresponding business data.
- Symptom: Calls to storage plugins fail in an intranet environment, with a prompt that resources cannot be accessed. Cause: The intranet access path for the plugin was not configured correctly. Using a public network access address prevented retrieval of locally stored device images and work order attachments.
How to confirm the configuration is correct
- Initiate a single tool call, pass sensor data and device ID for a single device, and check if the business data returned by the plugin matches the data in the local business system.
- Upload multiple device inspection images, and check if the plugin can correctly parse all image paths and generate corresponding results.
- Adjust the tool calling mode to
functionCall, pass structured parameters, and confirm that the plugin can receive and process parameters in the preset function format. - Test access to storage plugins in an intranet environment, and confirm that locally stored device-related files can be retrieved normally.
The values provided here are common starting points, and should be evaluated against the samples relevant to the specific deployment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.