What the Data for This Use Case Looks Like
The data related to aquaculture farming yield rates comes primarily from the national aquaculture technology promotion station’s aquaculture monitoring database, daily transaction ledgers from major producing area aquatic product wholesale markets, and IoT growth monitoring equipment at farming sites. Data update cadence falls into two categories: market trend data updates once daily at fixed times, while real-time monitoring data such as water quality and feeding amounts from farming sites updates every hour. The structure of individual data records includes the following fields: product category (e.g., Litopenaeus vannamei, grass carp), major producing area, purchase price, wholesale price, breeding cost breakdowns (feed, fry, labor), and slaughter cycle. Units are yuan per kilogram, yuan per tail, and yuan per mu respectively.
Constraints Imposed on Tool Calling and Plugins
The multi-category and multi-producing area distribution of aquaculture farming data requires that tool calling plugins support precise filtering by product category and producing area. Otherwise, redundant returned data will occupy the model’s context window. Daily-updated market trend data requires that the trigger timing of tool calls align with the data update periods, to avoid calling outdated, unupdated data. The nested breakdown structure of breeding costs requires that plugins have the ability to parse multi-level fields, which increases the complexity of parameter validation. The high-frequency update requirement for real-time monitoring data requires that plugins support flexible adjustment of call frequency, while balancing call frequency with interface rate limiting rules. Additionally, the returned content with multiple fields may exceed the model’s context window length, so corresponding truncation or pagination rules must be configured.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mcpServerProxyEndpoint | https://api.aquaculture-data.org/v1 | Connect to the standardized public market trend interface for the aquaculture industry, to avoid cross-domain access restrictions |
maxContext | 8000–12000 characters | Aquaculture data includes multiple categories and cost breakdowns, so sufficient context must be reserved to accommodate the complete field content returned by the tool |
toolCallInterval | 86400 seconds | Aligns with the daily update cadence of aquaculture yield rate market data, to avoid repeated calls to unupdated data |
toolCallFilterFields | product category, main production area, purchase price, total breeding cost | Filter non-essential fields to reduce the content length returned by the tool, adapting to context window limits |
responseParseTimeout | 30 seconds | Aquaculture data interfaces may experience response delays due to data synchronization lags in major producing areas, so reserve sufficient buffer time |
maxToolCallRetries | 2 times | Balance interface retry success rate and call frequency, to avoid triggering interface rate limits |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: Unable to create an MCP service, or MCP service connection fails. Cause: The address configured in
mcpServerProxyEndpointdoes not enable cross-domain support, or the correct interface authentication key is not filled in. - Symptom: Returns a 429 status code after triggering a tool call, or the interface call is rejected. Cause:
toolCallIntervalis set too short, making multiple calls to the aquaculture data interface in a short period trigger rate limits. - Symptom: Context overflow error occurs during tool calling, or the model cannot complete the tool calling process. Cause: The
maxContextparameter value is not limited, and the multi-category and multi-breakdown content of aquaculture data exceeds the context window length supported by the model, and automatic context truncation configuration is not enabled.
How to Verify Correct Configuration
- Access the configured
mcpServerProxyEndpointaddress, verify that the interface can normally return market trend data for aquatic product categories, to confirm interface connectivity. - Enable FastGPT’s tool call debugging mode, trigger a yield rate calculation request, and check whether the target product category and producing area information are correctly included in the tool call parameters.
- Check the tool call return logs, confirm that the returned fields match those configured in
toolCallFilterFields, with no redundancy or missing content. - Adjust the
maxContextparameter to the adaptation range of the maximum context window length supported by the current model, and verify that no context overflow error occurs during the tool call process.
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.