What the data for this category looks like
Data sources include state-owned asset supervision information systems, enterprise asset management systems, property rights exchange systems, valuation institution reports and financial statements. Daily business documents include property rights registration forms, asset valuation reports, property rights transfer plans, on-site transaction announcements, and asset disposal approval documents, with fields such as asset number, ownership status, valuation base date, and transaction approval node. The data update frequency is monthly. The overall data structure is mainly a mix of structured forms and unstructured reports, and there are slight differences in field naming across data from different sources.
What constraints do these characteristics impose on the "tool calling and plugins" link
Multi-source mixed data access requires tool calling to adapt to the parsing logic of both structured forms and unstructured reports, to avoid field matching deviations. The monthly update frequency requires the cache validity period of tool calling to align with the data update cycle, to prevent the use of expired data. The strict requirements for asset ownership and transaction data require that tool calling return results include data source traceability identifiers, to facilitate subsequent verification. Differences in field naming across different systems require configuring field mapping rules during tool calling, to reduce information misalignment. At the same time, the strong compliance requirements of business scenarios require that tool calling strictly limit the trigger scope, to avoid introducing irrelevant data into the context.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Tool Calling Trigger Condition | Trigger only when the question involves policy clause matching or ownership data verification | Matches the core business scenarios of this category to avoid invalid calls |
| Single Tool Calling Timeout | 600 seconds | Adapts to the time requirements of pulling data from multiple systems and parsing complex reports |
| Tool Return Result Parsing Rule | Extract corresponding fields classified by data source | Adapts to the parsing requirements of mixed structured and unstructured documents |
| Context Window Limit | 8000–12000 characters | Accommodates context content from multiple asset documents and original policy texts |
toolChoice | Automatic | Lets the model independently select tools suitable for the scenario, improving calling accuracy |
| Result Traceability Configuration | Attach data source identifiers | Meets compliance verification requirements in state-owned asset management scenarios |
Three Common Mistakes
- Phenomenon: When calling database tools, the token consumption displayed on the interface does not match the statistics from the API backend. Cause: The token statistics rule for tool calling is not configured, and there is a difference between the token calculation logic used in the model's internal reasoning and the actual API call.
- Phenomenon: After the workflow is executed, the results returned by the MCP tool cannot be passed to subsequent conversation nodes. Cause: The output mapping of the result variable is not configured in the workflow, causing intermediate data to not be correctly captured.
- Phenomenon: The results returned by tool calling do not match the user's question, and irrelevant content appears. Cause: The prompt does not clearly define the data source scope and matching rules for tool calling, causing the model to call irrelevant tools or data.
How to Confirm the Configuration is Correct
- Initiate a single tool calling test, verify that the fields in the returned results match the preset asset document fields.
- Check the tool calling logs to confirm that the token consumption statistics match the API backend logs.
- Simulate a question involving data from multiple systems, verify that the workflow can correctly obtain and integrate the results returned by the MCP tool.
- Adjust the test question scenario, confirm that the tool only triggers in the corresponding business scenario to avoid mis-calling.
The values provided are common starting points and should be measured 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.