Tool Calling and Plugins for Clinical Trial Pre-screening in

Data Characteristics for This Category

Clinical trial pre-screening data in market access originates from public databases of drug regulatory agencies (e.g., FDA, EMA, NMPA), clinical trial registries (e.g., ClinicalTrials.gov), and industry reports. Data updates frequently, typically weekly or monthly, as new drug approvals and clinical trial progress are released. Document structures vary, including structured database records, unstructured clinical study report PDFs, drug labels, and approval documents. Key fields include drug name, indication, trial phase, primary endpoint, secondary endpoint, subject inclusion/exclusion criteria, trial site, sponsor, approval status, and marketing authorization date. Units for dosage are commonly milligrams (mg) and micrograms (µg), treatment duration uses days, weeks, and months, and biological indicators have their respective international or standard units.

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

The wide range of data sources requires flexible tool calling to connect with various API interfaces, such as RESTful APIs for public databases or SDKs from industry data providers. High update frequency means the knowledge base needs frequent and incremental synchronization, and tool calls must handle data timeliness. Diverse document structures, especially the large volume of unstructured PDF reports, demand advanced OCR and information extraction plugins capable of accurately identifying trial details, subject criteria, and efficacy data. The specialized and varied nature of fields, such as complex inclusion/exclusion criteria text, necessitates customized text processing and entity recognition tools to ensure accurate pre-screening logic. Furthermore, differing approval processes across countries and regions require tools to call specific regulatory query plugins for more precise market access evaluations.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`API_TIMEOUT_SECONDS`	`60 seconds`	External API calls, especially when querying large databases, can have longer response times. This extends the timeout limit appropriately.
`MAX_TOKENS_PER_CALL`	`4096`	Clinical trial reports are text-heavy. This ensures a single call can process sufficient context, preventing truncation of critical information.
`CHUNK_SIZE` (for RAG)	`800–1200 characters`	Clinical trial data paragraphs are highly interconnected. This ensures chunks contain complete semantic units while avoiding excessive length that could reduce retrieval efficiency.
`RETRIEVAL_TOP_K`	`top 5`	Clinical pre-screening demands high information accuracy. Retrieving a small number of the most relevant, precise pieces of information reduces irrelevant interference.
`OCR_ACCURACY_THRESHOLD`	`0.85`	When processing scanned or image-based clinical trial reports, this ensures reliable text recognition and reduces error rates.
`PLUGIN_VERSION_COMPATIBILITY`	`v1.2.x`	This ensures compatibility with the current FastGPT platform version, preventing functional anomalies due to plugin version mismatches.

Three Common Pitfalls

Tool call returns "Cannot load image" or similar network error: This usually indicates network policy restrictions or improper proxy configuration when calling external services, preventing access to the target resource.
Plugin confirmation fails with error MongoServerError: The dollar ($) p: This typically means the plugin's required database version is incompatible with the MongoDB version in the FastGPT environment, leading to database operation syntax errors.
Querying patent information via a toolset returns "No patent information recorded for the enterprise," but direct testing of the toolset yields results: This might be due to a mismatch in parameter format or field names passed to the tool within the tool calling component, preventing the query conditions from being correctly transmitted.

How to Verify Configuration

Upload a typical clinical trial report PDF and use relevant plugins for information extraction. Check if key fields (e.g., primary endpoint, subject screening criteria) are accurately identified and extracted.
Simulate a market access pre-screening process, calling at least two external APIs (e.g., drug database and regulatory query). Verify that all tools successfully return data and that the returned data format and content meet expectations.
For clinical data containing complex time periods and dosage units, use tool calling to retrieve information. Verify that the tool correctly parses and understands the unit information.
Check FastGPT backend logs to confirm no HTTP 5xx error codes or Timeout warnings occurred during tool calls, and that plugin loading and execution were free of errors.

Note: 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.