Tool Calling and Plugins for Dermatology Registration and

Data Characteristics in this Domain

Dermatology registration and declaration documents involve diverse data types. These include clinical trial reports, pathological analysis reports, imaging data (e.g., dermoscopy images), in vitro pharmacodynamics and pharmacokinetics study data, adverse event monitoring data, and literature reviews. Data sources span hospital information systems (HIS), clinical trial management systems (CTMS), electronic health records (EHR), and specialized medical journals. Data update frequency varies by stage; clinical trial data accumulates continuously during trials, while post-market adverse event data updates are ongoing. Document structures are primarily structured and semi-structured, such as standardized clinical research reports (ICH-GCP). However, they also contain substantial unstructured text, like handwritten doctor's notes and transcribed patient interviews. Common fields and units include lesion area (cm²), skin lesion scores (e.g., EASI, SCORAD), drug concentration (ng/mL), and treatment cycle (weeks). Strict adherence to medical and pharmaceutical norms is essential.

Constraints Imposed by these Characteristics on Tool Calling and Plugins

The diversity of dermatology data requires tool calling to handle multimodal data. For instance, image recognition plugins must parse lesion characteristics from dermoscopy images. Data update frequency demands real-time capabilities from plugins, especially for adverse event monitoring. Plugins need to trigger data fetching and analysis periodically or on demand. The semi-structured and unstructured nature of documents means information extraction plugins must identify standard fields and extract key medical concepts from free text, such as specific dermatitis types and accompanying symptoms from doctor's notes. The standardization of fields and units requires post-processing modules in tool calling to perform unit conversions and data validation, preventing misinterpretations due to inconsistent units. Furthermore, the extensive use of specialized terminology and abbreviations challenges the accuracy and coverage of knowledge base and glossary plugins. This ensures the model correctly understands and references them.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`tool_max_retries`	`3`	Addresses transient external service failures, reducing call failures due to network fluctuations.
`tool_timeout_seconds`	`60 seconds`	Accounts for external API response speeds, preventing prolonged blocking.
`api_key_env_var`	`SKIN_DISEASE_API_KEY`	Manages sensitive information via environment variables, enhancing security.
`max_tokens_per_call`	`4000`	Accommodates dermatology report length, ensuring a single call can process complete paragraphs.
`search_result_limit`	`Top 5 entries`	Balances recall rate with processing cost, focusing on the most relevant medical literature or guidelines.
`mcpServerProxyEndpoint`	`Calibrate by actual measurement`	Ensures stable connection with the internal medical content platform server; the specific address requires configuration based on the actual deployment environment.

Three Common Pitfalls

When calling an external medical literature retrieval plugin, the returned result list is empty. This happens because the retrieval API's authentication information is incorrectly configured or query parameters are malformed.
The model attempts to call an image recognition tool to analyze pathological sections and encounters an HTTP 413 Payload Too Large error. This occurs because the uploaded image file size exceeds the tool interface's limit.
When processing clinical trial report PDF documents, the text extraction plugin frequently loses table data. This is due to the plugin's insufficient parsing capability for complex table layouts or because OCR functionality is not enabled.

Verification Steps

Check tool call records via FastGPT's log system. Confirm each call successfully returns an HTTP 200 status code.
Select a random dermatology declaration document containing multimodal data. Run an end-to-end process and verify that all expected fields are correctly extracted and formatted.
Execute a tool call for a field known to require unit conversion. Verify that the output result's unit has been converted or marked as expected.

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 2026-09-21.