What Data Looks Like for This Category
Text annotation project data primarily comes from client-delivered corpora, annotation samples produced by internal annotation platforms, custom vocabularies, and quality inspection sampling results. Data update frequency is daily, or after each batch of annotation tasks is completed. Daily associated documents include annotation specification documents, sample package files, quality inspection reports, and problem feedback forms. Core fields include original corpus text, entity annotation scope, intent classification tags, quality inspection markers, and problem details. There is no unified fixed unit; values adjust based on the annotation task type.
Constraints on Tool Calling and Plugin Workflows
Text annotation project data has scattered sources, flexible update rhythms, and diverse document structures. This requires the tool calling and plugin link to adapt to multi-source data pulling requirements, and support integration with different data sources such as annotation platforms and vocabulary libraries. Frequently updated annotation samples and quality inspection results require tool calling to support batch triggering and real-time synchronization, to avoid data lag. Diverse associated document types require plugins to support format parsing, enabling reading of files in different formats including annotation specifications and quality inspection reports. Flexible field structures require tool calling to support custom parameter mapping, adapting to the field requirements of different annotation tasks.
Configuration Guidelines
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto or a specified quality inspection and verification tool | Adapt to the tool calling requirements of text annotation projects, avoid forced calling of irrelevant tools |
functionCall | Enabled and configured with entity recognition and intent verification tools | Match the core task scenarios of text annotation, cover semantic boundary and classification verification |
| Tool call timeout | 300–600 seconds | Text annotation tool calls often involve batch sample processing, require sufficient execution time reserved |
| Batch call limit | First 20 samples | Avoid overloading a single call, adapt to the conventional batch processing scale of annotation tasks |
| Single call character limit | 800–1200 characters | Adapt to the single-sample length of conventional text annotation, avoid tool call overload |
Three Common Misconfigurations
- Phenomenon: Unable to send a termination command after calling an MCP server-side tool. Cause: The termination callback parameter is not enabled in the tool configuration, causing the server to fail to receive termination requests.
- Phenomenon: Interrupted output occurs when the model calls a tool. Cause: The number of samples processed in a single call exceeds the preset limit, triggering output truncation during execution.
- Phenomenon: Unable to obtain MCP tool execution records in the workflow via API calls. Cause: The tool call log storage configuration for the workflow is not enabled, causing logs to not be persisted.
How to Verify Successful Configuration
- Manually trigger a tool call, check if the returned result includes the structured fields required for annotation and quality inspection, confirm that the result format matches the configuration requirements.
- Call the termination command interface, verify whether the MCP server can correctly receive and terminate the ongoing tool task, confirm that the callback configuration is effective.
- View the workflow log module, confirm that the execution records of MCP tools have been properly stored, verify that the API call log acquisition permission configuration is correct.
- Simulate a batch sample call, check whether the tool call processes samples according to the preset limit, confirm that the batch configuration meets the task scale requirements.
The values provided here are common starting points and should be evaluated against samples relevant to the actual use case.
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.