Tool Calling and Plugins for Mental Illness Registration and

Data Characteristics for This Category

Registration and declaration data for mental illnesses originate from diverse sources. These include clinical trial reports, pharmacovigilance data, non-clinical study reports, epidemiological surveys, and various guidelines and consensus documents. Data update frequency is relatively low, primarily concentrating on new drug development and periodic post-market surveillance reports. Document structures are typically highly standardized, adhering to ICH guidelines and regulatory agency (e.g., NMPA, FDA, EMA) templates, such as the CTD (Common Technical Document) format. Fields and units are highly specialized, involving pharmacokinetics (e.g., AUC, Cmax in ng·h/mL), pharmacodynamics (e.g., PANSS scores, MADRS scores), adverse events (AE MedDRA coding), and patient-reported outcomes (PROs). This data often exists as a mix of structured tables, figures, and extensive unstructured text (e.g., clinician notes, patient interview records).

Constraints on Tool Calling and Plugins from These Characteristics

The data characteristics of mental illness declaration documents impose specific constraints on tool calling and plugins. First, the highly standardized CTD format requires plugins to accurately parse and extract deeply nested information, such as identifying key efficacy endpoints and their statistical significance from Module 2.5 Clinical Overview. Second, the highly specialized fields and units mean that general tools may not directly understand them. This necessitates customized parsers or domain-specific dictionaries as plugin pre-processing. For example, correctly identifying and processing scoring criteria and thresholds for psychiatric scales (e.g., HAM-D scale). Third, data update frequency is low, but the volume of data in a single update is large. This demands efficient batch processing capabilities from tool calling and the ability to handle large PDF or Word documents. Finally, unstructured text contains extensive clinical narratives. Plugins need advanced Named Entity Recognition (NER) and relationship extraction capabilities to structure this textual information, for example, identifying associations between drug dosage, administration route, and adverse reactions.

Configuration Settings

Configuration Item	Suggested Value	Rationale for This Value
`maxContext`	`8192` token	To process long paragraphs and multi-variable information in complex clinical trial reports, preventing context truncation.
`Chunk size` (Segment Length)	`1000–1200` characters	Balances semantic completeness and model processing efficiency, especially for parsing clinical records and research discussions.
`Recall count` (Recall Count)	`15` entries	Ensures retrieval of sufficient relevant regulations, guidelines, and previous declaration cases from the knowledge base.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Addresses the precision requirements for mental illness terminology, filtering out low-relevance information.
`Rerank result count` (Reranked Return Count)	`5` entries	Focuses on the most relevant regulatory clauses and key data points, reducing redundant input for the model.
`PARSE_FILE_TIMEOUT_SECONDS`	`600` seconds	To handle parsing of large PDF or Word format clinical study reports, preventing timeouts due to oversized files.

Three Common Mistakes

Receiving a 400 Bad Request status code when calling an external API. This occurs because medical terminology or parameter formats in the request body do not meet API expectations, for example, incorrect mapping of MedDRA codes.
Key data fields are empty in the model's output. This happens because the document parsing plugin failed to recognize non-standard table formats or data embedded in images.
An internal server error message appears after plugin import. This is due to Python dependency version incompatibility or missing necessary system environment variables in a private deployment environment.

How to Verify Correct Configuration

Select a CTD document containing typical mental illness clinical trial data. Run the document parsing plugin and check whether key pharmacodynamic indicators (e.g., PANSS score changes) are accurately identified and extracted in the parsing results.
Design a query containing specialized terminology and regulatory clauses. Use the tool calling function to obtain an answer and verify the accuracy of cited regulatory provisions and data sources in the answer.
Simulate a complete declaration document generation process. Check the logs of all external tools and plugin calls during the process to ensure no HTTP 5xx errors or prolonged response delays.

The values given 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.