What the data for this category looks like
The data for building construction engineering intelligent due diligence comes primarily from three official and third-party data sources: housing and urban-rural development department project filing systems, construction logs and material test reports from construction units, and acceptance records from supervision units. The data update frequency changes with project phases: construction progress is updated weekly during the active construction phase, and no frequent updates occur after filing is completed upon project completion. Each due diligence document includes five core modules: basic project information, construction progress details, material test reports, cost accounting sheets, and acceptance opinions. Fields include floor area (unit: square meters), project cost (unit: ten thousand yuan), start/completion dates (format: YYYY-MM-DD), supervision unit qualification number, and some fields have variants between common abbreviations and standard names.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The multi-source heterogeneous nature of building construction engineering due diligence data requires tool calling to simultaneously interface with housing and urban-rural development filing APIs, internal construction unit systems, and supervision data platforms, requiring independent authentication parameters to be configured for each data source. The phased update rhythm requires plugins to support incremental pull modes, avoiding request volume overload and timeout risks caused by full pulls. The multi-module, multi-field document structure requires tool calling to precisely specify returned fields, preventing redundant data from occupying transmission bandwidth and parsing resources. The fixed units and formats of fields require plugins to include built-in validation logic to match incoming parameters and returned results to specified formats, avoiding invalid due diligence reports caused by unit errors or format mismatches.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Building construction engineering due diligence documents typically include multiple construction logs and test reports, resulting in long parsing times |
tool_call_timeout | 600 seconds | When interfacing with multiple external data sources, a single call must cover the time required to pull and integrate housing and urban-rural development filing and supervision data |
external_tool_max_retries | 2 retries | Avoid triggering rate limiting rules for external interfaces through repeated calls, while ensuring stability of data pulling |
field_matching_threshold | 0.85 | Similar naming exists for building construction engineering fields (such as "floor area" and "planned floor area"), requiring relatively high matching accuracy |
plugin_request_batch_size | 5 items per call | Pulling too many project data items in a single request will exceed timeout thresholds; limiting batch pull quantity ensures calling stability |
response_field_filter | Specify project ID, floor area, project cost, acceptance status | Only return core fields required for due diligence reports, reducing data transmission and parsing overhead |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require individual analysis, and testing on local samples is recommended before finalizing settings.
Three Common Misconfigurations
- Symptom: A
504 Gateway Timeouterror code is returned when calling an MCP plugin, and the workflow node shows a timeout. Cause: Thetool_call_timeoutconfiguration was not adjusted, and the time required to pull multi-source data for building construction engineering exceeded the platform's default threshold. - Symptom: An empty set of core fields is returned after the workflow calls an external plugin, and the due diligence report cannot generate valid content. Cause: The
response_field_filterwas not configured, and redundant fields unrelated to due diligence requirements were returned, causing core data to be filtered or not correctly extracted. - Symptom: A workflow call using an API debugging tool fails, returning a
400 Bad Requesterror code. Cause: The unique ID generated by the system after workflow publication was not copied, and a custom name was directly used as the call parameter, causing the platform to fail to identify the target workflow.
How to Confirm Configurations Are Correct
- Navigate to the tool configuration page of the FastGPT workflow, check the values of
tool_call_timeoutandPARSE_FILE_TIMEOUT_SECONDS, and verify that they cover the time required to pull multi-source data for building construction engineering projects. - Initiate a test call, review the field list of the returned results, and confirm that only the core fields specified in
response_field_filterare included, with no redundant content. - Call the MCP plugin to pull complete data for a single building construction project, check for the
504 Gateway Timeouterror code, and confirm that the timeout configuration is effective. - Copy the unique ID generated by the system after workflow publication, initiate a call using an API debugging tool, and confirm that a correct workflow execution status is returned.
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.