What the data for this category looks like
Hotel and catering financial report analysis data mainly comes from store cash register systems, supply chain management ledgers, and headquarters business review reports. The data update rhythm follows three levels:
- Store-level revenue data updates daily
- Store summary data updates every ten days
- Quarterly financial report data is compiled within 5 working days after the end of each quarter
The document structure is a multi-dimensional structured table containing fields such as store_id, stat_date, total_revenue, food_cost, labor_cost, and customer_count. The corresponding units are: store ID string, date format, RMB yuan, RMB yuan, RMB yuan, and number of customers.
What constraints these characteristics impose on tool calling and plugins
Multi-dimensional structured financial report data requires strict field matching during tool calls to avoid mixing revenue and cost data. Data sources with different update rhythms require tools to support pull tasks configured for daily, ten-day, quarterly, and other cycles. The multi-store hierarchical data structure requires tool calls to include aggregation logic, enabling data filtering by headquarters, region, or individual store dimensions. Real-time fluctuating store-level revenue data requires clear specification of the statistical cycle during tool calls, to prevent analysis deviations from cross-cycle data mixing.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mcp_tool_timeout | 300 seconds | Hotel and catering financial report data typically aggregates multiple stores, with large single pull data volumes, requiring sufficient timeout allowance |
data_source_sync_interval | 86400 seconds | Store-level revenue data updates daily, so daily synchronization ensures data timeliness |
field_mapping_mode | strict | Financial report data fields must strictly correspond; loose mapping easily causes confusion between revenue and cost data |
max_result_count | Top 20 entries | Single-store monthly financial report data has a limited number of entries; limiting returned entries avoids context overflow |
api_token_quota_check | Enabled | Multi-tool calls require pre-verifying token consumption to prevent exceeding call quotas |
prompt_template_scope | Financial report analysis exclusive | Limit the prompt word scope to avoid the model calling irrelevant tools or generating non-financial report content |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common configuration errors
- The phenomenon is that when viewing tool call logs in a workflow, the displayed token consumption does not match the actual API billing token consumption. The cause is that the MCP tool's token statistics switch is not enabled, and only the token overhead of model interactions is counted.
- The phenomenon is that downstream workflow nodes cannot obtain structured data returned by the MCP tool. The cause is that field extraction rules for tool return values are not configured, preventing the model from correctly parsing the returned content.
- The phenomenon is that knowledge base-associated financial report analysis conversations return irrelevant content. The cause is that recalled documents do not limit the financial report data scope, or the similarity threshold is set unreasonably, resulting in recall of non-target documents.
How to confirm the configuration is complete
- Manually trigger an MCP tool call, and verify that the returned fields fully match the preset
field_mapping_moderules. - View the token consumption statistics panel, confirm that
api_token_quota_checkis enabled, and that single-call token consumption matches expected values. - Upload a test hotel and catering financial report dataset, initiate an associated query, and check that the model's returned content only focuses on financial report data.
- Configure a scheduled synchronization task, view the data source synchronization logs, and confirm that data updates according to the cycle set by
data_source_sync_interval.
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.