What the data for this category looks like
Crop farming marketing-related data comes from field soil and weather sensors, regional agricultural condition monitoring platforms, public weather APIs, and farming records voluntarily submitted by growers. Update cadences differ by data type:
- Weather data updates hourly
- Plot-level agricultural condition records update every ten days
- Crop growth records sync in real time with farming operations
Most document structures include fields such as unique plot identifier, crop variety, planting cycle, expected yield per mu, pesticide application records, and more. Units include mu, kilogram, degree Celsius, millimeter, and other agricultural-specific measurement standards.
What constraints do these characteristics impose on tool calling and plugins
The varying update cadences across data sources mean tool calls must adapt to multi-source data synchronization windows. Failing to do so can lead to using outdated weather or agricultural condition data, which reduces marketing content accuracy. The strong link between unique plot identifiers and per-mu parameters requires precise plot IDs to be passed as retrieval conditions during tool calls. Without these IDs, exclusive marketing content for the target farming scenario cannot be returned. Agricultural-specific units require the tool’s parameter parsing phase to support non-standard measurement formats. Unit conversion errors can otherwise cause content deviations. The fragmented nature of multi-source data requires plugins to support batch pulling and alignment of datasets from different sources. Without this capability, logically coherent crop farming marketing content cannot be generated.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
rag_retrieve_top_k | 8-12 results | Crop farming data has many fields and multiple associations between crops, plots, and farming activities. A sufficient number of relevant documents must be retrieved to match marketing needs |
plugin_timeout | 120 seconds | Pulling agricultural condition data requires calling multiple independent platform APIs, which takes longer than general scenarios. Extending the timeout threshold prevents call interruptions |
file_parse_chunk_size | 800-1000 characters | Farming record documents often contain long, continuous paragraphs of farming logs. Setting the chunk size too large will lose associated fields, while setting it too small will damage semantic integrity |
mcp_tool_enable_local | false | Crop farming marketing content relies on unified cloud-based agricultural condition data sources. Local tools cannot access cross-plot public agricultural data |
rag_similarity_threshold | 0.72-0.78 | Semantic matching for crop farming scenarios must account for both crop variety and plot location associations. A threshold that is too low will introduce irrelevant data, while a threshold that is too high will miss valid matches |
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. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Issue: Calling a local MCP tool returns
400 Bad Request. Cause: Crop farming marketing content requires calling cloud-based agricultural condition data sources. Local tools cannot access cross-plot public data, and themcp_tool_enable_localparameter is not configured correctly. - Issue: Calling an external knowledge base returns
No matching content. Cause: Cross-team knowledge bases do not have public retrieval permissions configured, and the corresponding knowledge base ID is not specified in the tool call parameters, preventing external data from being pulled. - Issue: Key farming fields are identified as empty when using an MCP tool to process a farming record Word document. Cause: The long paragraph characteristics of crop farming documents are not accounted for, and the
file_parse_chunk_sizevalue does not meet scenario requirements, leading to loss of core fields during document parsing.
How to confirm proper configuration
- Run a single tool call test, verify that the returned agricultural condition data includes exclusive fields for the target plot, and confirm that parameters are passed in accordance with configuration requirements.
- Check tool call logs to confirm that no timeout errors corresponding to
plugin_timeoutare triggered, and that the interface returns the expected status code. - Validate similarity matching results, adjust
rag_similarity_thresholdto a value suitable for the current scenario, and ensure that retrieved results cover both crop and plot associations. - Test cross-team knowledge base call permissions, confirm that external knowledge base related content can be pulled normally during tool calls.
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.