This configuration is based on the feature logic of FastGPT v4.17.0.
What the Data for This Category Looks Like
Production data for the pulp and paper category typically covers stock preparation, pulping, papermaking, quality, and energy segments. Daily documents include stock preparation sheets, beating records, paper machine operation logs, online quality curves, laboratory reports, and sheet break and grade change records. Data sources include wood pulp and waste paper batches, DCS/QCS systems, paper machine online measurement equipment, laboratory testing systems, and energy and water treatment systems. Data update frequencies include real-time (such as online quality curves), per shift (such as shift energy consumption statistics), and per roll (such as single roll paper quality reports). Common fields include stock ratio, beating degree, sheet grammage, moisture content, sheet break duration, and others, matching industry-standard production record formats.
What Constraints Do These Characteristics Impose on the Tool Calling and Plugin Link
The production data characteristics of the pulp and paper category impose multiple constraints on the tool calling and plugin link. First, real-time online data (such as paper machine operation parameters, online quality curves) requires tool calling to have low latency to avoid Q&A response timeouts. Second, multi-source heterogeneous data (such as bulk parameters from DCS systems, offline laboratory test reports) requires plugins to support cross-system data aggregation and unified formatting for AI processing. Third, data updated by batch or shift requires tool call results to be grouped by production dimension to match the query logic of process specifications. Fourth, abnormal data such as sheet breaks and grade changes requires plugins to support trigger-based calls for abnormal scenarios, assisting rapid troubleshooting of process issues. Additionally, some tools need to generate visual charts (such as online quality curve comparisons), requiring plugins to support conversion from structured data to visual formats.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Tool Calling Mode | Automatic Scheduling | Adapts to the dynamic tool identification needs of process problems, covering scenarios such as stock preparation and paper machine debugging |
| Single Tool Call Timeout | 600 Seconds | Adapts to the acquisition latency of real-time DCS/QCS data, avoiding timeouts for routine production data queries |
| Return Result Limit | Top 10 Entries | Adapts to the information carrying capacity of a single round of Q&A, avoiding excessive redundant data in outputs |
| Return Content Filter Rule | Retain only structured business fields | Remove unnecessary original logs and debug information, meeting the concise output requirements of Q&A |
| Tool Call Result Aggregation Method | Group by Production Batch | Matches the update rhythm of shift and per-roll production data, making it easier for process personnel to view data by batch |
| Workflow/API tool selection | auto | Allows the AI to independently identify required tool types, adapting to the needs of free tool scheduling |
| Workflow/API function-call configuration | enabled | Supports structured tool call parameters, ensuring the accuracy and traceability of tool calls |
Three Common Misconfigurations
- Phenomenon: HTML charts generated by pyecharts returned by tools cannot be displayed normally on the MCP host, returning a 404 status code. Cause: The MIME type of tool return content is not configured, causing the host platform to fail to recognize visual data in HTML format.
- Phenomenon: Original production logs returned by tool calls are directly output to users, exceeding the expected scope of Q&A. Cause: The return content filter rule is not configured, and unstructured debug logs and redundant data are not removed.
- Phenomenon: The AI cannot independently select the correct tool call, and can only fixedly call preset tools. Cause:
toolChoiceis not configured to auto mode, limiting the AI's tool scheduling capabilities.
How to Verify Proper Configuration
- Initiate a process query involving real-time paper machine parameters, verify that the trigger timing of tool calls matches expectations.
- Check the content returned by the tool, confirm that only the configured structured business fields are included, with no redundant original log data.
- Simulate a query involving visualization needs, verify that the chart format generated by the tool meets the display requirements of pulp and paper production data.
- Trigger an abnormal scenario query (such as sheet break record query), confirm that the AI can independently call the corresponding tool to complete the response.
The values provided here are common starting points and should be measured against the reader's own samples.