What this type of data looks like
Data sources for urban commercial bank intelligent due diligence reports include internal operating ledgers of urban commercial banks, regulatory information publicly disclosed by local financial regulatory bureaus, transaction data from cooperating credit granting institutions, and main credit data from third-party credit reporting agencies. Update frequencies vary: internal operating ledgers are updated daily, regulatory disclosure information is updated quarterly, and cooperating institution transaction data is updated weekly. Documents use structured tables as their core framework, which include three core modules: basic entity information, asset and liability details, and regulatory indicator ledgers. Most fields use "point-in-time value" and "cumulative amount" as identifiers. The unit is uniformly RMB ten thousand yuan.
Constraints imposed by these characteristics on tool calling and plugins
The varied update frequencies of multi-source due diligence data require tool calling to support different pull cycles per data source, to avoid repeatedly pulling low-update-frequency regulatory data. The precise naming requirements for structured fields mean that tool calling input parameters must match standardized field names. Fuzzy matching cannot be used for parameter binding. The uniform RMB ten thousand yuan unit requires plugins to have built-in unit validation logic, to prevent result deviations from cross-unit calculations. The need to connect multiple data sources requires MCP plugins to support dynamic switching of interface protocols and request headers, to adapt to different access rules for internal ledgers and public regulatory data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
max_tool_calls_per_round | 8–12 calls | Urban commercial bank due diligence reports require calling multiple types of data source tools. This range covers core indicator pull requirements and avoids single-call timeouts |
mcp_request_timeout | 600 seconds | Multi-source data pulling may involve batch interface requests. 600 seconds covers complete data acquisition time |
rag_tool_branch_rule | Triggered via keyword matching | Keywords for urban commercial bank due diligence such as "regulatory indicators" and "credit data" can clearly distinguish the calling scenarios of RAG knowledge bases and MCP tools |
mcp_input_param_validation | Enabled | Urban commercial bank data fields have a high degree of standardization. Enabling validation filters invalid input parameters and reduces interface call failure rates |
tool_response_max_length | 8000–12000 characters | Single indicator detail data for urban commercial bank due diligence is lengthy. This length covers complete structured returned content |
plugin_permission_scope | Only internal service accounts | Urban commercial bank due diligence data contains sensitive operating information. Call permission scope must be restricted |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing values.
Three Common Configuration Errors
- Phenomenon: After configuring the MCP service, custom HTTP response input parameter variables cannot be passed during tool calling. Reason: The custom parameter pass-through switch for the MCP plugin was not enabled, causing input parameter variables to be automatically filtered.
- Phenomenon: The large language model only calls knowledge base recall results during the due diligence report generation process, and does not trigger MCP tool calls. Reason: Precise RAG and tool call shunting rules were not configured, causing the keyword matching logic to fail.
- Phenomenon: Calling the FastGPT chat interface returns a
400 Bad Requesterror, prompting that the tool parameter format is incorrect. Reason: Tool call parameters were not encapsulated into thetool_callsarray format as required by the interface documentation, causing interface verification to fail.
How to Confirm the Configuration Is Correct
- Initiate a test request containing due diligence keywords, check if the system log shows MCP tool call records to confirm that the shunting rules are effective.
- Manually trigger MCP tool calls, check if the field names and units of the returned results meet the standardized requirements of urban commercial bank due diligence data.
- View the plugin permission configuration interface, confirm that the call permission scope only covers internal service accounts, and verify that the access restriction for sensitive data is effective.
- Call the FastGPT tool debugging interface, input preset due diligence parameters, and check if the returned content from the corresponding data source can be correctly obtained.
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.