What this type of data looks like
Cybersecurity level protection assessment consulting data comes from assessment tool outputs, on-site interview records, asset system inventories, and security device logs. Attach each data entry to the unique identifier of the corresponding assessment project. This ensures clear attribution and facilitates subsequent traceability of rectification evidence. Data updates occur once per project per year, and content is gradually improved as individual assessment projects progress. Standardized documents produced regularly include classification reports, gap analysis reports, rectification plans, and assessment reports. Data fields include control item identifiers, gap descriptions, rectification measures, and evidence association paths. All data must comply with standardized format requirements for cybersecurity level protection assessments.
What constraints do these characteristics impose on the tool calling and plugins workflow
Since data updates per project and has a long cycle, bind tool calling strictly to the project dimension. This avoids mixing data across projects. Standardized document structures require plugins to retrieve corresponding data by report type. This ensures output content matches the assessment process. Differences across multi-source data sources require plugins to support format conversion. This unifies output formats from different tools and logs. Assessment results directly link to regulatory inspection risks. Verify the accuracy of tool call returns to prevent incorrect data from affecting review conclusions. For external tool calls, confirm connection status is normal. This ensures stable data pulling.
The following configurations are formulated based on FastGPT v4.17.0.
How to set these configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto or specified target plugin ID | Supports automatically matching tools required for the review workflow, or forcing a specified plugin to be called, adapting to the fixed process of cybersecurity level protection assessments |
functionCall | Enabled | Supports large models in outputting structured review results, facilitating the generation of standardized assessment reports |
| Single tool call timeout | 120 seconds | Cybersecurity level protection assessment data comes from multi-source logs and system inventories, requiring sufficient time for data pulling and parsing |
| Plugin call frequency limit | 1 time per project per hour | Data updated per project does not require frequent calls, avoiding unnecessary resource consumption |
| Tool call context binding scope | Current project dimension | Prevents mixing of data across projects, aligning with the business rule that cybersecurity level protection assessments are carried out per individual project |
| MCP server deployment status | Enable as needed | External tool calls rely on MCP server for cross-domain connection and data pulling; internal tools do not require deployment |
Three common misconfigurations
- A cross-domain error occurs when calling the
api/v1/chat/completionsinterface. The cause is that FastGPT’s cross-domain whitelist is not configured, and the front-end domain name is not allowed to access the interface. - The error "chat: llm-model-response-empty" is returned during the call flow. The cause is that the structured data returned by the tool call does not properly populate key fields, preventing the large model from generating a valid reply.
- No cybersecurity level protection assessment data is retrieved after enabling the plugin. The cause is that the plugin is not bound to the corresponding project’s data source, or the data source update cycle does not match the tool call frequency.
How to confirm the configuration is correct
- Initiate a test call. Check if the cross-domain error in the interface response disappears. This confirms the cross-domain configuration is effective.
- Trigger a tool call. Review if the returned structured data includes cybersecurity level protection assessment-specific fields such as control item identifiers and gap descriptions. This confirms the
functionCallconfiguration is effective. - View tool call logs. Confirm only the current project’s data source is loaded, and no cross-project data mixing occurs.
- Verify the MCP server connection status. Confirm no connection exceptions occur when calling external tools. This confirms the on-demand deployment configuration is effective.
- Simulate a single tool call timeout scenario. Confirm the call completes within the preset 120 seconds or triggers a timeout prompt. This verifies the timeout configuration is effective.
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 2026-10-19.