What Data for This Category Looks Like
Dairy industry research report data comes primarily from domestic securities firm research institutes, monthly reports from the China Dairy Industry Association, regular financial reports of listed dairy enterprises, and sales movement data from industry monitoring institutions. Update cadence has multiple tiers: in-depth securities reports are released quarterly, monthly tracking reports are updated alongside industry trends, and ad-hoc reports are triggered by major events such as raw milk price fluctuations. Industry association reports are updated once per month. Corporate financial reports are synchronized quarterly.
Document structure is mostly structured content, including industry overviews, core data tables, corporate updates, policy interpretations, and trend analysis. Core fields include raw milk purchase guide price (unit: yuan/kg), liquid milk production capacity (unit: thousand tons), and revenue of key enterprises (unit: 100 million yuan). Some documents also include channel coverage data.
Constraints Imposed on Tool Calling and Plugins
The multi-source update cadence, structured table format, and dispersed data sources of dairy industry research reports impose clear constraints on tool calling and plugin configuration. Differences in update cycles across multi-source data require plugins to support tiered refresh triggers, to avoid excessive system resource consumption. Fixed units and core data fields require tools to validate return formats during calls, to prevent unit confusion or missing fields. Dispersed data sources require connecting multiple plugin interfaces, so tool chains must support multi-step calls and context passing. Redundant data filtering is also needed to improve response efficiency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
tool_call_timeout | 300 seconds | Dairy industry research reports require simultaneous calls to securities research report APIs, industry association data interfaces, and corporate financial report interfaces. The average response delay across multi-source interfaces is high; 300 seconds covers the full call chain |
max_tool_call_rounds | 3 rounds | Dairy industry research report retrieval requires three core tool call steps: data fetching, table parsing, and result integration. Three rounds cover the full process and avoid invalid loops |
structured_parse_mode | Table First | Core data in dairy industry research reports is presented in structured tables. Prioritizing table parsing accurately extracts key metrics like raw milk prices and production volume |
plugin_refresh_interval | 7 days | The average update cycle for industry monthly reports and weekly sales movement data is 7 days. Setting a 7-day refresh interval balances data timeliness and system resource usage |
tool_result_fields | raw milk price, production volume, revenue | The core requirement for dairy industry research report retrieval is core business indicators. Limiting returned fields reduces interference from irrelevant data in responses |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: After calling a tool, the response includes input and output logs from knowledge base searches, instead of only displaying organized research report conclusions. Cause: The
tool_call_output_filterparameter is not configured, or the context hiding switch for tool call results is not enabled, causing original tool call logs to be included in the final response. - Phenomenon: After importing a plugin via CURL, no confirmation submit button appears in the interface, making plugin creation impossible. Cause: Required
nameandauth_typefields are not included in the CURL request, or the request format does not comply with FastGPT plugin import specifications, so the interface cannot recognize valid configurations. - Phenomenon: When calling the MCP client to retrieve dairy industry data, numeric type fields are returned as string format, or the API call returns a
403 Forbiddenerror code. Cause: The automatic numeric type conversion switch is not enabled in the MCP plugin configuration, or IP whitelists and valid API keys for application calls are not configured, leading to abnormal data formats or permission verification failures.
How to Confirm Proper Configuration
- Send a single dairy industry research report retrieval test request, check the tool call chain logs, confirm only the configured 3 rounds of tool calls are triggered, with no extra loop calls.
- Enter the plugin management interface, check the last refresh time of the plugin, confirm the update cycle matches the configured
plugin_refresh_intervalvalue. - Call the configured MCP client interface, verify that returned fields such as raw milk price and production volume are numeric formats that can be directly used for calculations.
- Use the generated API key to send an application call request, confirm the returned result only includes organized research report conclusions, with no irrelevant logs or permission error messages.
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.