What the data for this category looks like
Auto service sector yield rate and market data primarily comes from internal corporate financial accounting systems, bulk export interfaces from auto finance partners, and third-party auto industry market data APIs. The data update rhythm completes a full sync of the previous day’s data every early morning, making this T+1 cycle daily report data. Each individual data entry follows standard JSON format, including fields such as service_category (service type, e.g. financial lease, maintenance package), stat_period (statistical cycle), yield_value (yield rate value), store_id (associated store identifier), update_time (data update timestamp). Statistical dimensions differ across various service categories.
What constraints these characteristics impose on tool calling and plugins
The daily report update rhythm requires tool calls to adapt to the T+1 scheduling logic, to avoid frequent pulling of unupdated data. The multi-data-source and multi-service-category characteristics require tool calls to support multiple sets of authentication configurations and category-based filtering parameters, otherwise data mixing or permission leakage issues may occur. The multi-dimensional field design requires tool calls to configure field filtering rules to simplify the processing logic of returned results. Additionally, auto service data often involves sensitive business information associated with stores or customers, so additional permission isolation rules must be configured to ensure that callers can only access data within their authorized scope.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
mcp_data_source_auth | Configure multiple sets of authentication parameters per data source, with each set bound to a service_category filtering rule | Auto service yield rate data comes from multiple partners and internal systems, requiring authentication isolation by service category |
tool_call_timeout | 600 seconds | Reserve sufficient interface response time when pulling yield rate data for multiple stores in bulk |
api_permission_scope | store_id:{{current_user.store_ids}} | Isolate data by store dimension to prevent cross-store permission leakage |
scheduled_task_cron | 0 3 * * * | Match the daily 02:00 update rhythm of daily report data to trigger tool calls on a scheduled basis |
response_field_filter | Retain service_category, stat_period, yield_value, update_time | Filter irrelevant fields to simplify tool returned results and improve calling efficiency |
cache_expire_seconds | 86400 seconds | Match the update cycle of daily report data to avoid repeated pulling of unupdated data |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: The API call returns
403 Forbidden, and the response fielddetailreads "Insufficient permissions". Cause: Theapi_permission_scopeparameter is not configured, or the configured store range does not match the caller's actual permissions. - Symptom: Target data cannot be obtained after connecting the MCP plugin in the workflow, and the log shows "Data source not authorized". Cause: No authentication key for the corresponding auto service data source is configured in
mcp_data_source_auth, or the correctservice_categoryfiltering rule is not bound. - Symptom: Tool calls continue to time out, eventually returning
504 Gateway Timeout. Cause: Thetool_call_timeoutparameter is not set, or the set value is less than the actual interface response time, without accounting for the extra time required to pull data for multiple stores in bulk.
How to confirm the configuration is successful
- Call the test interface with a specified store ID, check whether the returned results only include auto service yield rate data for that store, and whether the fields match the
response_field_filterrules. - View the authentication logs of the MCP plugin to confirm that the authentication parameters for the corresponding data source have been correctly loaded, with no unauthorized related errors.
- Manually trigger the tool call, record the actual response time, and adjust the
tool_call_timeoutparameter to a reasonable range greater than this response time. - Check the scheduling logs of the scheduled task to confirm that the tool call triggered at the specified time each day has successfully pulled and processed the full daily report data from the previous day.
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.