Tool Calling and Plugins for Phase II-III Clinical Trial

Data Characteristics for This Category

Phase II-III clinical trial prescreening data primarily originates from sponsor-provided study protocols, inclusion/exclusion criteria documents, patient medical records, historical examination reports (e.g., imaging, pathology), and laboratory test results. Data update frequencies vary. Study protocols and inclusion/exclusion criteria are typically defined during trial design and change infrequently. Patient medical records and examination reports are continuously generated as subjects enroll and during follow-up, leading to high update frequencies. Document structures vary. Study protocols often exist as PDFs, containing extensive unstructured text. Medical record data may be provided as EHR system export files (e.g., HL7, CDA) or structured tables (CSV, Excel). Fields require standardized handling of medical terminology, units of measurement (e.g., mmol/L, ng/mL, mmHg), and time formats.

Constraints Imposed by These Characteristics on "Tool Calling and Plugins"

The data characteristics of Phase II-III clinical trial prescreening impose specific requirements on tool calling and plugins. First, the large volume of unstructured study protocols and medical record text demands robust text parsing capabilities from tools. Tools must accurately extract key information from complex medical text, such as disease diagnoses, medication history, and surgical history. Second, the real-time nature of data updates requires tool calls to handle streaming data or high-frequency batch data synchronization to ensure prescreening results are based on the latest subject status. Third, standardization of medical terminology and units is critical. Tools need to incorporate or integrate medical dictionaries to unify synonyms and units from different sources, preventing misjudgments due to inconsistent data formats. Finally, for structured data, tools must support flexible query and filtering operations, such as filtering by numerical indicators like age, BMI, and creatinine levels.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000`	Clinical protocol documents are often long, requiring a larger context window to accommodate complete information for understanding.
`Chunk size` (Segment Length)	`1000 characters`	Ensures the integrity of medical concepts, preventing truncation of critical information.
`Similarity threshold` (Similarity Threshold)	`0.75`	Improves recall precision, reduces interference from irrelevant medical record data, and is suitable for strict matching of medical text.
`Rerank result count` (Reranked Return Count)	`Top 5`	Focuses on the most relevant subjects or medical record snippets, reducing subsequent processing burden and improving efficiency.
`toolTimeoutSeconds`	`60 seconds`	Processing complex medical text parsing and database queries can be time-consuming; this provides sufficient execution time.
`toolInputSchema.type`	`object`	Ensures the tool can receive structured multi-parameter input, such as query conditions containing multiple fields.

Three Common Pitfalls

The tool calling module does not execute as expected, and logs show Tool call failed: missing required parameter. This occurs when the input_schema in the tool definition does not strictly match the parameters passed during actual invocation, especially when parameter type constraints do not align with actual values.
The tool calling module in the workflow fails to reference knowledge base content after connecting to it. This typically happens because the output of the tool calling module is not correctly mapped to the input of subsequent knowledge base queries, or the knowledge base query parameters are not configured correctly, preventing the transfer of information extracted by the tool to the knowledge base.
When processing large volumes of patient medical record data, tool calls frequently encounter Connection timed out errors. This may be because the tool execution time exceeds the toolTimeoutSeconds threshold, especially when the tool performs complex database queries or remote service calls.

How to Verify Correct Configuration

Use simulated calls or a test set to verify that the tool accurately identifies inclusion/exclusion criteria in study protocols and extracts key fields from simulated medical record data, such as diagnosis, medication, and laboratory indicators.
Examine tool call logs to ensure all expected parameters are correctly passed and that tool return values conform to the expected data structure and format, particularly regarding the accuracy of medical unit conversions.
Run a test case containing various data types (structured, unstructured, different time formats) to confirm that tool calls execute stably in all complex scenarios and do not produce 4XX or 5XX status code errors.
Compare tool output with manual review results to evaluate the accuracy and recall of prescreening, and set corresponding qualification thresholds based on business requirements.

Note: The values provided are common starting points. Measure against your 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.