What the data for this category looks like
Industrial park yield rate data mainly comes from rent collection ledgers of the self-owned properties of park operating entities, industrial building monitoring systems from local commercial departments, and public industrial real estate rental market reports. The update rhythm is mostly monthly. Some key investment parks update derivative data such as vacancy rate on a weekly basis. Most documents are structured tables, with fields including park location, property type, leased area, actual rent per unit area, operating cost items, accounting cycle, and others. Rent unit price is usually measured in yuan/square meter·day. Operating costs include property fees, energy fees, and others, with units of ten thousand yuan/month. Accounting cycle is usually natural month or quarter.
What constraints these characteristics impose on tool calling and plugins
Industrial park yield rate data characteristics impose multiple constraints on the tool calling and plugin workflow. First, the mostly monthly update frequency requires tool calling scheduled tasks to match the data update rhythm. This avoids calling outdated unupdated data or excessive resource usage. Second, the fixed field structure of structured tables requires plugin parameter mapping to strictly correspond to specified fields such as park location, property type, rent unit price, etc. Field mapping rules cannot be adjusted arbitrarily. Third, data from multiple sources have unit differences. Plugins must include unified conversion logic to align different units such as yuan/square meter·day and ten thousand yuan/month. Fourth, the diversity of property types requires tool calling classification parameters to cover types like standard workshops, R&D buildings, incubators, etc., to adapt to different yield calculation rules.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
toolCallMaxRetries | 2–3 times | Industrial park data sources are relatively stable. Too many retries add invalid calls, too few fail to cover temporary interface fluctuations |
maxContext | 8000–12000 characters | Industrial park structured data has many fields. Sufficient context is needed for tool calling parameter verification and result parsing |
pluginCacheTTL | 2592000 seconds (30 days) | Matches the monthly update frequency of industrial park data, avoids expired caches or retained outdated data |
fieldMappingStrict | Enabled | Industrial park data fields vary widely. Strict mapping prevents calculation errors caused by field misalignment |
toolCallTimeout | 600 seconds | Some park operation system interfaces respond slowly. Sufficient time must be reserved for data pulling and conversion |
pluginInputSchema | Preset classification fields by park property type | Adapts to the accounting needs of multiple industrial park property types. Ensures tool calling input parameters meet business scenario classification requirements |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Issue: Tool call parsing fails, and the console returns the
Tool call Parser nerror. Cause: The model's inference content is wrapped inthinktags and placed in thecontentfield. This does not follow the standard tool calling format requirements, so the parser cannot recognize the calling instruction. - Issue: The number of results returned by the tool call does not match expectations, only partial park data is returned. Cause: No reasonable period is configured for
pluginCacheTTL, or the scheduled task period does not match the data update frequency. This results in calling outdated cached data or missing some monthly updated park projects. - Issue: Context overflow error is triggered during tool calling. Cause: The
maxContextparameter is not adjusted to the range suitable for industrial park data length. Excessively long structured data exceeds the model's supported context limit, causing the call to interrupt.
How to Confirm the Configuration is Correct
- Check the tool call logs to confirm that each call's input parameters fully match the preset
pluginInputSchemafields, with no missing or misaligned fields. - Trigger a manual tool call, compare the returned structured data with the fields and units of the original park ledger, and confirm that the unit conversion logic takes effect.
- Check the scheduled task execution period, confirm that it aligns with the industrial park data update frequency, with no early or delayed calls.
- Test tool call retries in abnormal scenarios, confirm that the
toolCallMaxRetriesconfiguration covers temporary interface fluctuations, with no repeated calls or unresponsive situations.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.