What the data for this category looks like
State-owned asset management company ledger data originates from three primary sources: the state-owned asset supervision comprehensive information system, regular submissions from wholly owned and holding subsidiaries, and the company’s own financial asset management system. Data update cycles are divided into three categories: monthly (for daily asset changes), quarterly (for full inventory checks), and annual (for ownership change summaries). Each ledger document includes eight core fields: asset category, book original value, accumulated depreciation, net asset value, usage status, ownership certificate number, affiliated enterprise, and supervision level. Units include ten thousand yuan, square meters, sets/units, pieces, and others. Data is classified and summarized at two levels: by enterprise and by asset category, with a clear hierarchical structure.
What constraints these characteristics impose on tool calling and plugins
Ledger data for state-owned asset management companies is scattered across multiple sources with varying update cycles. This requires tool calling to connect to multiple MCP servers simultaneously to pull data from different channels. Fixed formats and unit requirements for core fields mean tool calling must perform strict validation on incoming parameters to prevent aggregation failures caused by format errors. Annual ownership change data has a large volume, so tool calling must support batch pulling and incremental synchronization to avoid single-request timeouts. Permission controls for classified asset ledgers require tool calling to bind role permissions, only allowing authorized users to trigger corresponding tools.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Allows the model to autonomously select tools suited for state-owned asset ledger aggregation, supporting multi-source data pulling requirements |
functionCall | Mandatory enable | Ensures tool call parameters generated by the model comply with fixed format requirements for ledger fields |
| Tool call timeout duration | 120 seconds | Adapts to time consumption for multi-source data pulling, preventing early request termination |
| Tool call retry count | 2 times | Mitigates call failures caused by network fluctuations or temporary system lag |
| MCP server address configuration | Fill in the docking addresses for the state-owned asset supervision system and self-owned financial system | Connects to multi-source ledger data sources to enable full data aggregation |
| Tool parameter validation switch | Enabled | Validates field formats and units of incoming parameters to ensure compliance with ledger specifications |
Three Common Misconfigurations
- Calling an MCP tool returns a "parameter format mismatch" error. This occurs because the
functionCallmandatory validation is not enabled, and parameters generated by the model do not match the fixed units and formats of ledger fields. - Output interruptions occur during tool calling. This happens because the trigger logic for
toolChoiceis not properly configured, and the model fails to correctly connect data flow steps when calling multiple tools. - API calls to workflows cannot retrieve MCP tool execution details. This is because the workflow's tool call log return switch is not enabled, and log fields for API return parameters are not configured.
How to Verify Proper Configuration
- Manually trigger the workflow to call the ledger aggregation tool, check if the returned result includes all preset core fields and if units comply with state-owned asset ledger specifications.
- In the FastGPT v4.17.0 backend tool management page, confirm that the
toolChoiceandfunctionCallconfiguration parameters match the preset values. - Call the workflow via the official API, check if the returned result includes MCP tool execution records and parameter details.
- Simulate passing non-standard asset number parameters, confirm that the tool triggers validation and returns a clear error prompt.
The values listed above are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.