What the data for this category looks like
Data sources for air governance intelligent due diligence reports include publicly available online monitoring point data from ecological environment departments, project environmental impact assessment (EIA) approval documents, governance facility operation and maintenance logs, and compliance reports from third-party testing institutions.
Update frequency varies by data type:
- Online monitoring data updates hourly or in real time
- Operation and maintenance logs are archived per project operation cycle
- EIA documents are static, and only update when project changes occur
Document structure falls into three categories:
- Monitoring ledgers include fields such as monitoring time, point number, and pollutant concentration
- Operation and maintenance records include entries such as equipment number, operating duration, and maintenance content
- EIA approvals include clauses such as project scope and pollutant emission standards
All field units follow official ecological environment department specifications. Pollutant concentration uses mg/m³ as the unit. Operating duration uses hours as the unit.
What constraints do these data characteristics impose on the tool calling and plugins workflow
The data characteristics of the air governance category impose multiple specific constraints on the tool calling and plugins workflow. First, real-time or high-frequency online monitoring data requires tool calling request frequencies to adapt to the data source's rate limiting rules, to avoid triggering access restrictions. Second, multi-source and heterogeneous document types (PDF-format EIA files, CSV-format monitoring ledgers, JSON-format real-time data) requires plugins to support multi-format parsing and unified field mapping, to ensure the model can correctly read data from different sources. Third, unified field units requires built-in unit verification logic during tool calling, to avoid due diligence data distortion caused by unit conversion errors across data sources. Fourth, massive operation and maintenance logs from large projects requires plugins to support paginated pulling and incremental synchronization, to avoid timeouts caused by loading too much data in a single call.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MCP_REQUEST_TIMEOUT | 300 seconds | Batch pulling of air governance monitoring data usually involves requests for multiple points. 300 seconds covers response durations for most scenarios |
HTTP_TOOL_MAX_BODY_SIZE | 100 MB | Monthly monitoring ledger CSV files for large air governance projects usually do not exceed 100 MB. Adapting to this size avoids upload failures |
RECALL_CHUNK_SIZE | 800–1200 characters | The length of valid single pieces of information in air governance monitoring data falls within this range. This avoids context fragmentation caused by overly short chunks |
PARSE_FILE_MAX_ROWS | 50,000 rows | Monthly operation and maintenance logs for most medium-sized air governance projects do not exceed 50,000 rows. Adapting to this parameter avoids parsing timeouts |
TOOL_CALL_MAX_RETRIES | 3 times | For temporary rate limiting of third-party monitoring interfaces or network fluctuations, 3 retries covers most temporary failure scenarios |
LOG_LEVEL | debug | Detailed calling logs for the MCP service need to be tracked to facilitate troubleshooting of abnormal issues during due diligence report generation |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: An error "file parameter not supported" is prompted when calling the HTTP tool to upload a monitoring ledger file, with a 400 status code returned. Cause: The file was not converted to base64 encoding before being passed to the
filefield, or the HTTP tool's file upload permission switch was not enabled. - Symptom: Only "call successful" is displayed in the logs for the MCP component in the workflow, with no detailed request parameters or return results. Cause:
LOG_LEVELwas not configured asdebug, and the detailed log output function of the MCP service was not enabled. - Symptom: When generating a due diligence report for a large air governance project, MCP call duration exceeds the preset threshold, and model response delay is obvious. Cause: The amount of data pulled per MCP call was not limited, and historical monitoring data exceeding business needs was loaded in batches, resulting in excessive model processing load.
How to confirm correct configuration
- Initiate an HTTP tool call targeting a local air monitoring point, check if the returned response body contains expected pollutant concentration data, and confirm that the configured
HTTP_TOOL_MAX_BODY_SIZEadapts to the target file size. - Start the MCP service and trigger a due diligence report generation task, check if the workflow logs include detailed parameters and return results of the MCP call, and confirm that the
LOG_LEVELconfiguration is correct. - Configure a workflow to connect the RAG knowledge base and MCP tool, set the trigger condition as uploading the EIA file for the due diligence project, check if the workflow executes the data pulling, knowledge base recall, and report generation steps in sequence, and confirm that parameter transfer between nodes is correct.
- Simulate a single batch MCP call to pull monitoring data, check if the call duration meets business expectations, and adjust the matching relationship between
MCP_REQUEST_TIMEOUTand the single pull data volume.
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.