What the Data for This Category Looks Like
Livestock and poultry farming market and profitability data is primarily sourced from fixed-point monitoring stations of the National Animal Husbandry Station, publicly available industry data submitted by local animal husbandry authorities, and daily sampling data from professional breeding monitoring institutions. Data update frequency varies by indicator: slaughter price and daily average feed price are updated daily; inventory volume and monthly breeding cost statistics are updated weekly. Each data entry uses structured JSON format, containing fields such as livestock and poultry category, monitoring region, breeding scale level, core cost items, slaughter weight, and average slaughter price. Corresponding units for each field are: category name, administrative region level, breeding subject type, yuan/kg, kg, yuan/kg.
What Constraints These Characteristics Impose on the Tool Calling and Plugins Workflow
The multi-source and multi-update frequency characteristics of livestock and poultry farming data require the tool calling workflow to adapt to different scheduling cycles, preventing triggering data source current limiting from overly high pull frequency. The multi-dimensional field structure requires tool input parameters to support multi-condition filtering by category, region, scale level and other criteria, reducing redundant data pull volume. Differences in field naming across data sources require configuring unified field mapping rules to ensure consistency in subsequent profitability calculations. The sampling nature of some data may cause fluctuations in the number of results returned per batch, requiring configuration of result deduplication and verification logic to avoid duplicate calculations or data deviations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MCP_CALL_TIMEOUT | 300 seconds | Livestock and poultry farming multi-site aggregated data pull takes a long time; 300 seconds covers most conventional pull scenarios |
MAX_TOOL_RETRIES | 3 times | Breeding data sources may experience temporary network fluctuations; 3 retries reduces the probability of call failure |
TOOL_INPUT_SCHEMA | "Must include livestock and poultry category, monitoring region, and core cost fields" | Missing core fields prevents execution of profitability calculation logic; verification intercepts invalid requests early |
PARSE_DATA_MAPPING | Custom field mapping rules to unify cost item naming | Differences in cost field naming across data sources; unified mapping ensures consistency in subsequent data processing |
MAX_TOOL_RESULTS | "Top 15 entries" | Returning too much data per batch increases context processing burden; 15 entries balances data completeness and call efficiency |
TOOL_RESULT_VALIDATION | Enable duplicate data deduplication | Some data sources have cross-site duplicate submitted data; deduplication avoids profitability calculation deviations |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- After tool calling returns array<object> formatted breeding data, it is not possible to directly generate readable broadcast text. The cause is that no format conversion rules are configured for the data parsing plugin, preventing the large language model from directly processing structured array data.
- MCP service calls fail frequently, and logs only show timeout status codes. The cause is that no reasonable
MCP_CALL_TIMEOUTparameter is set; breeding data pull time exceeds the configured threshold, causing call interruption. - After configuring HTTP input parameter variables for MCP, requests fail to carry custom parameters normally. The cause is that the names of input parameter variables do not match the request body field names required by the plugin, leading to parameter mapping failure.
How to Confirm Proper Configuration
- Initiate a single tool call test, verify that the returned results include the preset core fields, and confirm that the field units match business requirements.
- Submit a multi-dimensional filtering request, confirm that the returned results only match the configured filtering criteria, with no redundant data outside the scope.
- View tool call logs, confirm that the retry count and timeout configuration are properly loaded, with no unhandled exception errors.
- Trigger the data format conversion process, confirm that the structured array is correctly converted to readable text, with no missing fields or formatting issues.
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.