Process Specification and Technical Standard Q&A for Non-Ferrous Metal Smelting and Electrolysis: Tool Calling and Plugins

Data sources for this category include concentrate and auxiliary material analysis reports, production DCS systems, electrolyzer voltage and current

Data Characteristics for This Category

Data sources for this category include concentrate and auxiliary material analysis reports, production DCS systems, electrolyzer voltage and current monitoring data, laboratory test results, and energy and environmental protection monitoring ledgers. Update frequency changes with production nodes. Real-time updates occur for each batch of raw materials, each smelting furnace run, or each electrolysis cell operation. Daily generated documents include batching lists, smelting records, electrolyzer status records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Common fields include concentrate grade, cell voltage, current intensity, and impurity content. Units include percentage, millivolt, ampere, and grams per tonne, among others.

Constraints for Tool Calling and Plugins

This category of data has high real-time performance and scattered update nodes. Tool calling must support low-latency responses to avoid disrupting production scheduling due to waiting timeouts. Daily document fields are professional and varied. Plugins must accurately match the field formats of specific documents such as batching lists and electrolyzer status records, and cannot rely on generic field mapping. Data sources cover multiple systems. Tool calling must support integration with multiple types of data sources including DCS and laboratory systems, and dynamically pull the latest production data without relying on static cache files. Critical data involves metal recovery rates and environmental protection indicators. Tool-extracted fields must strictly align with the units and precision requirements of the original ledgers.

Configuration Guidelines

This configuration applies to FastGPT v4.17.0.

Configuration ItemRecommended SettingRationale
Workflow/API tool selectionautoAdapts to dynamic tool calling needs during process Q&A, no manual specification of specific tools required
Single tool call timeout300 secondsMatches the response waiting window of production processes, prevents Q&A interruptions caused by data pull delays
Tool return field filteringRetain core fields such as concentrate grade, cell voltage, and impurity contentMatches core business fields of documents for this category, reduces interference from invalid information
Workflow/API function-call configurationCalibrated based on actual measurementsAdapts to function call formats for professional process terminology, avoids parameter parsing errors
Plugin data source refresh frequencyEvery 10 minutes / Real-time triggerCorresponds to the update rhythm of production data, ensures Q&A uses the latest production data

Three Common Configuration Errors

  • Setting the stream parameter to true during interface calls prevents retrieval of full final results. This occurs because aggregation logic for streaming data is not configured, leading to unmerged segmented returned data.
  • An internal server error prompt appears when importing plugins in a private deployment environment. This is caused by a mismatch between the deployment version and the plugin’s required environment configuration, or uncleared local cache.
  • MCP plugins wrapped with the SSE protocol cannot be recognized normally. This happens because the platform only supports plugins of the stdio process type, and no protocol adaptation configuration adjustments have been completed.

How to Verify Successful Configuration

  • Submit a test request containing process parameter queries, and check whether the returned results include preset core business fields.
  • Review plugin operation logs to confirm that tool call response times meet the preset waiting window requirements.
  • Perform a connectivity test after importing the plugin, and confirm that no abnormal error prompts are displayed.
  • After configuring streaming calls, verify that all segmented returned result data can be fully received and aggregated.

The values provided are common starting points and should be measured against the reader's own samples.