What data for this use case looks like
The data for environmental monitoring intelligent due diligence reports primarily comes from publicly accessible monitoring stations under ecological environment departments, self-built sewage outlet sensors of enterprises, and mobile monitoring collection devices. Regular station data updates at hourly intervals, while key regulated locations can update at minute intervals. Each data document includes fields such as monitoring point code, pollutant name, real-time concentration, sampling time, and quality control flag. Some documents also include point latitude and longitude, and affiliated administrative region information. Concentration field units mostly use industry standard units such as μg/m³ and mg/L.
What constraints these characteristics impose on tool calling and plugins
High-frequency minute-level data updates require tool calling to adapt to short-interval requests, to avoid obtaining outdated data. Multi-source heterogeneous data sources require plugins to support connecting to monitoring APIs with different permissions, and dedicated authentication parameters must be configured. Fixed fields and units require strict matching of parameter names and unit formats during tool calling, to avoid data parsing errors. The presence of the quality control flag field requires adding filtering logic after tool calling, to eliminate invalid monitoring data that failed quality control.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
tool_request_timeout | 300 seconds | Environmental monitoring data updates frequently; an overly long timeout will cause returned data to be outdated, affecting the timeliness of due diligence reports |
max_tool_calls_per_round | 3–5 times | The number of monitoring data sources that need to be called for a single due diligence is limited; excessive calls will increase overall response latency |
stream_response | false | Due diligence reports require complete aggregation of all monitoring data; streaming returns will make fragmented splicing difficult, affecting report completeness |
tool_param_mapping | Calibrated based on actual measurements | Different monitoring APIs have varying parameter naming conventions; core fields such as monitoring point code and pollutant name must be matched |
validate_quality_flag | Enabled | Invalid monitoring data that failed quality control must be filtered out, to ensure the validity and credibility of due diligence report data |
api_detail_level | full | Due diligence reports require complete details such as sampling time and concentration values, to cover all data dimensions required for due diligence |
The parameter values provided on this page are general recommendations for starting point configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to conduct tests on local samples before finalizing settings.
Three common misconfigurations
- Phenomenon: Significant differences exist between online conversation and API call return results. Field missing still occurs when
stream=falseanddetail=trueare set on the API side. Cause: Parameter mapping is not configured for the multi-field structure of environmental monitoring data, resulting in incorrect transmission of field filtering rules in API requests. - Phenomenon:
getaddrinfo ENOTFOUNDerror is returned when calling an external monitoring API. Cause: The API domain name entered in the plugin is not fully configured or contains spelling errors, preventing the system from completing domain name resolution. - Phenomenon: A 400 error is returned when calling an MCP tool with an Xinference model, but the model runs normally when no tool is called. Cause: The monitoring data format returned by the MCP tool does not match the input format required by the model, resulting in model parsing failure.
How to confirm configuration is complete
- Initiate a single tool call request, check if the returned monitoring data fields include core information such as monitoring point code, concentration value, and sampling time, and verify that the field units comply with environmental monitoring industry standards.
- Simulate a multi-source data aggregation call, confirm that all configured plugins can return data normally, with no
getaddrinfoor timeout errors. - Enable the quality control verification switch, call test data with invalid quality control flags, and confirm that the system automatically filters out invalid entries.
- Compare the return results of online conversation and API calls, confirm that their core data fields and formats remain consistent.
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.