Tool Calling and Plugins for Solid Tumor Clinical Trial

Data Characteristics in this Domain

Data for solid tumor clinical trial pre-screening primarily comes from clinical trial registries (e.g., ClinicalTrials.gov), medical literature databases (e.g., PubMed, Embase), genomics and molecular biology databases (e.g., TCGA, COSMIC), and hospital electronic health record systems. Update frequencies vary. Clinical trial registration information typically updates in real-time or daily. Medical literature updates according to journal publication cycles. Genomic data updates are relatively slower. Document structures are diverse. Clinical trial protocols include free-text descriptions and structured fields (e.g., inclusion/exclusion criteria, study design). Genomic data often stores in specific formats like VCF and BAM. Electronic health records contain unstructured progress notes and structured lab and imaging results. Fields and units involve tumor size (millimeters/centimeters), biomarker expression levels (percentage, copy number), and drug dosage (milligrams, milligrams/kilogram). Unit standardization varies.

Constraints on Tool Calling and Plugins from these Characteristics

Data source heterogeneity and varying update frequencies require tool calling to integrate multi-source data and handle data recency. For example, real-time queries to ClinicalTrials.gov might take precedence over locally cached medical literature data. Diverse document structures mean plugins need to support various parsers. Parsing free-text inclusion/exclusion criteria requires natural language processing capabilities, while parsing VCF files requires specific bioinformatics tools. Inconsistent fields and units require tool calling to perform unit conversion or standardization during parameter passing, preventing logical errors due to non-uniform units. For example, when screening patients, the system must recognize and convert tumor size units from different sources. Furthermore, scenarios involving sensitive patient information impose higher security and compliance requirements on tool calling.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`8000 tokens`	Processing complex clinical trial protocols and multiple patient records requires a larger context window to maintain information integrity.
`Chunk size`	`500 characters`	Clinical trial inclusion/exclusion criteria often contain lengthy descriptive text. This length helps maintain semantic completeness.
`Recall count`	`Top 8 entries`	Solid tumor pre-screening conditions are complex. Recalling more relevant rules and guidelines from the knowledge base increases matching success.
`Similarity threshold`	`0.75`	Clinical trial screening demands high precision. A higher threshold ensures recalled results are highly relevant to the query.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large clinical trial protocols or genomic data files can take a long time. This avoids timeout interruptions.
`Rerank result count`	`Top 3 entries`	After recalling many items, re-ranking selects the most relevant few, improving tool calling accuracy.

Common Pitfalls

Tool call fails, and the AI model directly generates a response without retrying or prompting the user: This happens because the prompt template before tool calling does not explicitly instruct the AI on how to handle failures, or the max_retries parameter is set too low.
Inaccurate clinical trial screening results, failing to match eligible patients: This occurs because the descriptions of inclusion/exclusion criteria in the knowledge base are imprecise or ambiguous, or the Similarity threshold is set too low, leading to the recall of irrelevant rules.
File upload or parsing times out when processing large genomic reports: This happens because UPLOAD_FILE_MAX_SIZE or PARSE_FILE_TIMEOUT_SECONDS parameters are set too small, failing to accommodate large file processing needs.

Validation Steps

Simulate various complex queries to verify if tool calling accurately identifies and invokes corresponding external APIs or plugins. For example, query the eligibility of patients with specific gene mutations for a certain targeted therapy clinical trial.
Check if parameters passed to external tools during tool calling are correct. For example, verify if tumor size units are standardized and if biomarker expression levels fall within the expected range.
Compare the screening results returned by the FastGPT platform with manual control results. Evaluate if the matching accuracy meets the predefined business threshold.
Review system logs to confirm no timeouts or unhandled errors occur when processing extreme cases (e.g., large file parsing, multi-source data integration).

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.