Tool Calling and Plugins for Environmental Monitoring Yield

What Data Looks Like for This Category

Data sources include the National Ecological Environment Monitoring Network, IoT monitoring stations, and public APIs from third-party environmental data service providers. Update cadence is minute-level sampling. Daily reports generate full aggregated data for the previous day in the early morning each day. Data is provided in structured JSON or CSV format, including monitoring point ID, monitoring timestamp, pollutant concentration metric name, concentration value, administrative region of the monitoring point, and associated yield-related indicator fields linked to regional environmental governance. Units include μg/m³, degrees Celsius, hectopascals, ppm, and others. Each record corresponds to multiple monitoring metrics for one monitoring point at a single time point. Daily report data is aggregated by point and time, with no redundant invalid fields.

Constraints Imposed on Tool Calling and Plugins

Multiple data sources require the tool calling plugin to support configuring multiple data source addresses and authentication parameters, with automatic failover when the primary data source is unavailable. Minute-level sampling and daily report aggregation require tool calling to support filtering data by time range, and adapt to single-batch data volume limits during bulk pulls. Structured data with multiple fields requires the plugin's input parameters to support specifying monitoring points and metric types, and output parameters to support field filtering. Time-series data requires tool calling to support sorting by timestamp to avoid out-of-order data. Fixed daily report generation time requires the plugin to support scheduled trigger pull tasks, to fit daily yield and market report broadcasting scenarios.

How to Set Configurations

Configuration Key	Recommended Value	Rationale
`plugin_data_source_url`	Fill in the public API address of the National Ecological Environment Monitoring General Station	The data source is authoritative and requires no additional authentication, fitting public scenario requirements
`plugin_auth_type`	`api_key`	Most third-party environmental data service providers require API key-based authentication
`plugin_batch_size`	`100 records`	Single-batch data volume adapts to tool call response speed, avoiding overly long single request latency
`plugin_timeout`	`30 seconds`	Most environmental monitoring API responses typically take 10-20 seconds, with buffer time reserved for network fluctuations
`plugin_filter_fields`	`["Point ID","PM2.5 Concentration","Monitoring Time","Regional Yield Related Indicators"]`	Only return fields required for broadcasting, reducing data transmission volume and improving processing efficiency
`plugin_schedule_cron`	`0 0 6 * * *`	Fits the requirement to pull the previous day's daily report data at 6 AM daily for broadcasting
`plugin_max_retry_count`	`2 retries`	Address temporary network fluctuations, avoiding excessive repeated pulls that impact overall efficiency

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 individual analysis. It is recommended to test on your own samples before finalizing values.

Three Common Configuration Errors

For FastGPT open source version v4.8.20-fix2, the symptom is that after adding a custom plugin to a workflow, input and output parameters do not appear in the interface, with no error logs generated. The root cause is that the plugin_input_schema and plugin_output_schema fields are not correctly filled in the plugin configuration, preventing the system from parsing parameter mapping rules.
The symptom is slow response when calling the chat interface externally, with error code ETIMEDOUT. The root cause is that plugin_batch_size is set too large, pulling too much environmental monitoring data in a single request, which increases data transmission and processing time.
The symptom is that AI sessions run in a loop but do not process data as expected. The root cause is that the environmental monitoring point list is not passed to the loop body in standard array format, causing the loop trigger condition to fail to match the data structure.

How to Verify Successful Configuration

Navigate to the FastGPT plugin management page, check the plugin_input_schema and plugin_output_schema fields, confirm that parameter names and types match the environmental monitoring data fields.
Trigger a plugin test call, verify that returned data fields and units match expected environmental monitoring metrics.
After configuring the scheduled task, check workflow task logs to confirm plugin call response time meets expectations.
Call the external chat interface, confirm returned results include correct environmental monitoring data and associated yield-related fields.

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.