What the data for this category looks like
Coal mine production ledger data comes from four sources: underground environment sensors, fully mechanized mining equipment operation logs, team inspection reports, and dispatching center production reports. Data updates follow three rhythms: underground sensor data updates every 10 seconds. Team inspection reports are submitted per shift. Monthly production ledgers are finalized by the 5th of each month. Single ledger documents include fields such as working face number, equipment ID, operating duration, gas concentration, coal tonnage energy consumption, fault count, inspection time, and consumable usage quantity. Units are standardized as follows: gas concentration uses %VOL, coal tonnage energy consumption uses kilograms of standard coal per ton, and operating duration uses hours.
What constraints these characteristics impose on the tool calling and plugin workflow
Multi-source heterogeneous data sources require tool calling plugins to support both streaming real-time data pulling and batch historical data import. Full ledger summarization requirements cannot be met without this support. The specificity of fields and units requires that parameter mapping for tool calling must strictly match preset fields. Otherwise, data format errors will occur, and compliant production reports cannot be generated. Mixed calls for data with different update frequencies increase the load on the context window. If call rounds and context length are not restricted, context overflow issues are likely to be triggered. Dispersed underground equipment data interfaces require plugins to configure multi-tool routing to avoid single call request timeouts.
How to configure the settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
toolChoice | auto | Adapts to multi-tool calling scenarios, automatically selects either equipment data pulling or report generation tools |
| Single tool call timeout period | 300–600 seconds | Addresses network latency and device response times for underground data interfaces |
| Context window length | 8000–16000 tokens | Accommodates multi-shift ledger data and associated safety inspection report content |
functionCall | Forced to enable | Ensures model output complies with tool calling format requirements and avoids parsing errors |
| Maximum tool call rounds | 3–5 rounds | Limits repeated calls and avoids context overflow and invalid computation |
Three common configuration mistakes
- Tool call return content is wrapped in think tags, resulting in a Tool call Parser error. The cause is that the model's output content field wraps thinking content, causing tool call parameters to fail to be extracted correctly.
- Context overflow is triggered during tool calling, with empty return results or a prompt that the context length is exceeded. The cause is that tool call rounds are not restricted, or overly long historical ledger data is not truncated.
- Models connected via aiproxy fail to forward tool call requests normally, returning long error codes. The cause is that proxy tool call forwarding rules are not configured correctly, or key permissions do not include tool calling permissions.
How to confirm the configuration is correct
- Submit a test request that includes a specified working face number and shift, and check whether the corresponding data pulling tool can be called correctly and matching field content is returned.
- View tool call logs to confirm that the
toolChoiceandfunctionCallconfigurations are loaded correctly, and no parameter parsing related error prompts are present. - Simulate a test request that includes multi-shift ledger data, and check whether reasonable context truncation or round restrictions are triggered, and no context overflow error is reported.
- Test the proxy connection scenario to confirm that tool call requests can be forwarded normally to the target model, and no abnormal error codes are returned.
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 2026-09-28.