What the data for this category looks like
Data for footwear financing daily reports comes from footwear supply chain finance SaaS platforms, partner bank corporate credit systems, and warehouse and distribution logistics ledgers. It syncs full data from the previous calendar day every early morning. The document uses a structured table format. Each record corresponds to daily financing-related information for a single SKU, and includes five core fields: sku_code (character type), product_category (such as casual shoes, outdoor shoes), financing_amount (unit: RMB yuan), payment_term (unit: calendar days), warehouse_receipt_count (unit: number of documents). No redundant nested fields are included.
What constraints these characteristics impose on tool calling and plugins
The structured SKU-level field characteristics of footwear financing daily reports require that sku_code or product_category be specified as filter conditions during tool calling. This avoids timeouts caused by pulling full data sets. The daily T+1 update schedule requires that plugin scheduled tasks be configured to trigger after 3 a.m. daily. This ensures complete data from the previous day is obtained. The non-nested field structure of single records reduces plugin parsing complexity. Field names must be strictly matched to complete data extraction, to avoid parsing failures caused by mismatched field names. Footwear SKU categories are concentrated in a small number of classifications such as casual shoes and outdoor shoes. Classification parameters can be used to narrow the calling scope and reduce invalid data transmission.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
tool_call_filter_fields | ["sku_code", "financing_amount"] | Only retain core fields required for footwear financing daily reports, to reduce the volume of invalid data returned by tools |
max_tool_calls_per_round | 3 | Footwear SKU categories are limited, so a maximum of 3 calls per round covers financing data queries for core categories |
tool_timeout_seconds | 600 seconds | Pulling full SKU data may take a long time. Set a longer timeout to avoid mid-run interruptions |
mcp_input_mapping | {"target_sku": "{{input.sku_code}}", "date_range": "yesterday"} | Map user-input SKU codes to MCP service input parameters, to adapt to the SKU-based query requirement for footwear |
workflow_tool_split_rule | Trigger tool calls when input contains sku_code, otherwise call the knowledge base | Distinguish traffic between financing daily report data queries and general footwear financing knowledge consultations |
python_api_request_timeout | 1200 seconds | Reserve sufficient response time for Python interface calls when pulling batch SKU data |
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, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Issue: Unable to add HTTP response input parameters when configuring MCP services, and the interface prompts
invalid_param_formaterror. Cause: Input parameter mapping is not defined in the JSON format required by FastGPT, and input variables are not wrapped in{{}}syntax. - Issue: The large language model does not trigger MCP tool calls and returns general answers. Cause: The
tool_call_thresholdparameter is not set, or the threshold is set too high, causing the large language model to not meet the tool call trigger condition. - Issue: The traffic split between tool calls and knowledge base calls in the workflow fails, and all requests call the knowledge base. Cause: The
workflow_tool_split_ruleconfiguration is incorrect, and the trigger identifier (such assku_code) for footwear financing daily report queries is not correctly matched.
How to confirm the configuration is complete
- Enter the FastGPT MCP service configuration page, check the
mcp_input_mappingparameter, and confirm that user-input SKU codes are mapped to MCP service input variables. - Initiate a test request containing a valid
sku_code, check the tool call log, and confirm that the returned fields match the core fields of the footwear financing daily report. - Test a general consultation request that does not include
sku_code, and confirm that the system calls the knowledge base instead of tools. - Run the Python call script, check that the interface returns a
200 OKstatus code, and there are no timeout or parameter error prompts.
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.