Tool Use and Plugins for Solid Tumor Regulatory Submission

Data Characteristics for This Category

Solid tumor regulatory submission documents involve diverse data types. These primarily include clinical trial reports, pathology reports, imaging data, gene sequencing results, and pharmacokinetic and pharmacodynamic data. Data sources include Hospital Information Systems (HIS), Laboratory Information Management Systems (LIMS), Electronic Data Capture (EDC) systems, and Picture Archiving and Communication Systems (PACS). Data update frequencies vary; clinical trial data updates incrementally with trial progress, while pathology and gene sequencing data generate once after sample analysis. Document structures typically follow ICH guidelines and national regulatory agency CTD (Common Technical Document) formats, such as Modules 2, 3, 4, and 5. Field and unit standardization is strict. For example, tumor size is in millimeters (mm), efficacy assessment uses RECIST 1.1 criteria, and gene mutation sites use HGVS nomenclature.

Constraints Imposed by These Characteristics on Tool Use and Plugins

The complex data characteristics of solid tumor regulatory submission documents impose specific requirements on tool use and plugins. First, multi-source heterogeneous data requires plugins with robust data integration capabilities. These plugins must parse data formats from different systems, such as converting DICOM imaging data into analyzable structured information. Second, frequently updated clinical trial data demands real-time or near real-time data synchronization mechanisms for tool calls, ensuring submissions use the latest data. The CTD document structure requires plugins to understand and navigate complex document hierarchies, precisely locating needed information. Strict field and unit standardization means plugins must be highly accurate in data extraction and validation. For instance, they must differentiate between mm and cm and identify and correct non-RECIST compliant statements. Identifying and comparing specific gene mutation sites also requires plugins with bioinformatics processing capabilities to ensure data accuracy.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`8192 token`	Ensures the model can process complete clinical trial report summaries or multiple key sections, preventing information truncation.
`Recall Count`	`Top 10`	Increases the probability of retrieving critical evidence and supporting documents from vast submission data, covering more potentially relevant documents.
`Similarity Threshold`	`0.78–0.85`	Filters out documents that are semantically irrelevant despite similar medical terminology, reducing noise while ensuring relevant content recall.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides sufficient parsing time for large PDF clinical study reports or multi-page pathology reports, preventing timeouts that lead to file parsing failures.
`Segment Length`	`1000–1500 characters`	Accommodates the long paragraph characteristics of CTD documents, maintaining semantic integrity and preventing key information from being split across different segments.
`Rerank Return Count`	`5`	Further improves the ranking of the most relevant information from initial recall results, ensuring the model prioritizes the most valuable evidence.

Three Common Mistakes

Tool calls return 400 <400> InternalError.Algo.InvalidParameter: The tool errors. This commonly occurs when the parameter structure or data type passed to the plugin does not match the plugin's expectation, such as passing a string to a field requiring a number.
Plugin execution results in empty or incomplete output. Key information is missing, or data fields are not populated. This may occur if the plugin fails to correctly parse specific report formats, such as scanned PDF documents whose content cannot be recognized by OCR.
The model generates generic answers without triggering tool calls when external data support is needed. This may occur if the model misunderstands the tool description or if the user's query intent has low matching with the tool's functionality.

How to Verify Correct Configuration

Select a solid tumor clinical study report containing various data types (e.g., text, tables, chart descriptions). Test if the plugin accurately extracts all key fields and compare with the original report to confirm extraction accuracy.
Create a simulated list of missing items for a submission document. Use the tool call function to test if the model can locate and complete these missing items by querying relevant documents and databases. Record the response time.
Use a pathology report containing specific gene mutation information as input. Verify if the plugin can identify and correctly extract mutation sites and related descriptions. Cross-reference the extracted results with the original report.
Simulate data update scenarios for clinical data from multiple sources (e.g., EDC, LIMS). Check if tool calls can promptly synchronize the latest data and reflect it in query results, ensuring data timeliness.

The values provided are common starting points and should be measured against specific 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.