What the Data for This Category Looks Like
The data for text annotation projects mainly comes from customer business corpus, annotation samples produced by in-house annotation platforms, custom vocabularies, and quality inspection sampling results. Core daily documents include annotation operation specifications, batch sample packages, quality inspection result reports, and problem feedback registration forms. Data updates are triggered daily or after a single batch annotation task is completed. Fields typically include text content, corresponding annotation tags, entity boundary information, and quality inspection verification results. No additional mandatory units are required.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The data characteristics of text annotation projects impose multiple constraints on tool calling and plugin configuration. Dispersed multi-source data sources require plugins to support pull rules adapted to different data sources such as customer corpus and annotation platform samples. The mapping logic of configuration input parameters must be clearly defined. The diverse formats of daily documents require plugins to support parsing of unstructured content such as annotation specifications, sample packages, and quality inspection reports. Additional field alignment logic must be implemented. The daily or batch data update rhythm requires tool calling to support dynamic pulling of the latest annotation data. Using fixed historical datasets should be avoided, as this can affect review accuracy. Especially in code change review scenarios, differences between the latest annotation samples and historical versions must be compared synchronously.
How to Configure
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapts to the FastGPT v4.17.0 tool calling framework, fits the code review assistance scenario for text annotation projects, and automatically determines whether to call tools to handle tasks such as annotation sample comparison and quality inspection result verification. |
functionCall | Triggered based on annotation task type | Configure calling rules for corresponding tools for different review scenarios such as entity annotation and boundary verification, to ensure tool calling matches current review requirements. |
| Single tool call timeout | 600 seconds | Annotation project review tasks usually involve comparing multiple documents. Reserve sufficient time to complete data pulling and parsing, to avoid timeout interruptions. |
| Tool call context window | 8000–12000 token | Adapts to the context input requirements of multiple documents such as annotation specifications and sample packages, covering complete review reference data. |
| Tool return result limit | Top 10 entries | Control the number of samples or quality inspection results returned by tools during the review process, to avoid excessive information interfering with large model judgment. |
Three Common Misconfigurations
- Phenomenon: Unable to add HTTP response input parameter variables for MCP services. Reason: The dynamic input parameter mapping function is not enabled in the tool configuration, and fields such as annotation task batch ID and quality inspection score are not bound as transferable input parameters.
- Phenomenon: Annotation samples returned during tool calling have incomplete image display. Reason: The resource loading timeout parameter for tool calling is not configured. The large model returns results before sample images from the annotation platform are fully loaded, resulting in content truncation.
- Phenomenon: MCP service calls frequently fail or have no response, and the large model does not trigger tool calling as expected. Reason: The data update rhythm of the text annotation project is not adapted. The input parameters of tool calling are not bound to the latest annotation batch identifier, causing the service to fail to match the corresponding data. At the same time, the
toolChoiceparameter configuration does not meet expectations, and the tool trigger conditions cannot be correctly associated.
How to Verify Correct Configuration
- Initiate a simulated code change review request, input a prompt containing annotation sample comparison requirements, and check whether the tool can correctly pull the annotation sample data of the corresponding batch.
- View the tool call log, and confirm that the input parameters of the MCP service have been correctly bound to fields such as annotation batch ID and quality inspection score.
- Verify that the
toolChoiceparameter is set toauto. The large model will actively trigger tool calling when encountering scenarios requiring annotation sample comparison. - Test an annotation sample review request containing images, and confirm that the tool can fully load and return the image content.
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.