What the data for this category looks like
Data sources for this category include raw material batch ledgers, production MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory testing equipment. Daily generated documents include batching records, coating and rolling parameters, winding or stacking records, liquid injection records, formation and grading data, and OCV and safety test reports. Data is updated in real time per production batch and per individual cell. Electrochemical systems have extremely high requirements for moisture, cleanliness, and batch consistency. The post-stage formation and grading links have long data generation cycles.
What constraints these characteristics impose on tool calling and plugins
Data sources are scattered and have high real-time requirements. Tool calling must connect to multiple heterogeneous data sources and adapt to protocol formats of different devices. Single batch and single cell data have fine granularity, with a large number of fields to process per call. The return scale and storage upper limit of single calls must be limited. The sensitivity of electrochemical systems requires that the field precision of call results matches the unit specifications of production parameters. For example, slurry solid content and rolling thickness must correspond to precise measurement standards. The long-cycle nature of formation and grading data requires plugins to support batch pulling and associated query of historical data, avoiding repeated calls to inefficient interfaces. Tender documents must fill in data in sequence according to production links, so tool calling must follow the sequential logic of the production process.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | Fixed as auto | Adapts to calling scenarios with multiple heterogeneous data sources, automatically matching tools for corresponding production data |
| Number of items returned per tool call | 6–10 | Matches the field scale of single-batch cell production data, avoiding overload of returned content |
| Tool call timeout threshold | 90–150 seconds | Adapts to the long-cycle query requirements of formation and grading data, avoiding timeout interruptions |
| Single-batch data pull upper limit | 400–600 MB | Matches the storage scale of single-batch cell production data, avoiding memory overload |
| Workflow/API function-call configuration | Enable parameter validation | Ensures that returned data such as slurry solid content and rolling thickness complies with unit specifications for lithium battery production |
| Multi-tool call sequence | Sort by production process | Matches the requirement that tender documents must fill in data in sequence according to links such as batching, coating, and assembly |
Three common mistakes
- Phenomenon: The LLM does not call MCP tools in the specified order in one query, with returned results in chaotic order. Cause: The execution order rule for multi-tool calls is not configured, or the dependency logic of the production process is not bound.
- Phenomenon: FastGPT fails to start, and logs show MCP service connection timeout or protocol mismatch. Cause: The MCP service address and port in the FastGPT configuration are not updated, or related service processes are not restarted after an upgrade.
- Phenomenon: Fields returned by tool calls are empty or have incorrect units, and cannot be filled into tender documents. Cause: The parameter validation mode of
functionCallis not enabled, and the dedicated field mapping rules for lithium battery production data are not bound.
How to confirm the configuration is complete
- Test call a single type of production data tool, check whether the returned fields and units match lithium battery production specifications, and confirm that the validation function of
functionCallis effective. - Initiate a test query containing multiple types of data, check whether tool calls are executed in the order of the production process, and confirm that the multi-tool sequence configuration is correct.
- Simulate upgrading the MCP service and restart FastGPT v4.17.0, check that the service logs have no connection errors, and confirm that the MCP service configuration matches the current version.
- Check the number of returned items and the size of pulled data for tool calls, confirm that they do not exceed the preset single-call upper limit, and confirm that the return scale complies with the configuration requirements.
All provided values are common starting points, and should be evaluated against the reader’s own samples.