Tool Calling and Plugins for Bioequivalence Quality

Data Characteristics

Bioequivalence (BE) study quality documents include research protocols, ethics approvals, clinical trial reports, statistical analysis reports, regulatory approvals, contracts, subject informed consent forms, and batch inspection reports. These documents are typically PDFs, Word files, or scanned images. Updates are infrequent, occurring primarily at project initiation, during mid-term changes, and at project conclusion. Document structures are complex, containing numerous tables, charts, and unstructured text. Key fields include drug name, dosage, number of subjects, primary pharmacokinetic parameters (e.g., Cmax, AUC0-t, AUC0-∞), statistical analysis results (e.g., 90% confidence interval, geometric mean ratio), batch number, and production date. Units commonly include milligrams (mg), milliliters (mL), hours (h), and micrograms per milliliter (µg/mL).

Constraints on Tool Calling and Plugins

Bioequivalence document characteristics impose specific requirements on tool calling and plugin configurations. Documents are often unstructured or semi-structured, requiring robust file parsing capabilities. Scanned documents necessitate accurate OCR tools, which directly impacts the quality of subsequent information extraction. Pharmacokinetic parameters and statistical results typically appear in tables, requiring accurate table structure recognition and specific cell data extraction. Parameter names like Cmax and AUC are industry-specific, requiring precise matching during tool calls. Low update frequency means data sources are usually historical archives, so API calls must ensure data source stability and version management. Document information such as batch numbers and production dates may require cross-validation with external databases. This necessitates tools capable of calling external interfaces for data comparison or queries, such as invoking an internal LIMS system API for batch information.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Bioequivalence documents are large and complex, requiring more parsing time to prevent timeouts.
`Chunk size` (Segment Length)	`800–1200 characters`	Document paragraphs are long and contain complete research descriptions; longer segments help maintain contextual integrity.
`Recall count` (Recall Count)	`Top 10`	Ensures enough relevant document segments are recalled for complex queries, covering key parameters and conclusions.
`Similarity threshold` (Similarity Threshold)	`0.75`	A high threshold helps filter out irrelevant general text, focusing on specific bioequivalence study data.
`OCR_ENGINE_TYPE`	`TencentCloud OCR`	Handles large volumes of scanned documents and table/chart data within images, requiring high-accuracy OCR services.
`http_timeout`	`60 seconds`	When calling external LIMS systems or other validation interfaces, sufficient response time is needed to avoid timeouts due to network latency or complex query processing.

Common Pitfalls

getaddrinfo ENOTFOUND errors during external tool calls typically indicate that the domain or IP address configured for the tool is unresolvable. Verify the MCP_API_URL or other external service address configurations.
A 400 error from the large language model when calling a specific tool (e.g., MCP) indicates that the request parameter format or content does not comply with the tool's API specification. Compare the JSON request body sent to the tool against the API documentation.
Key fields (e.g., Cmax, 90% confidence interval) are empty in the tool's return results. This may be due to inaccurate extraction of specific data from tables or text during document parsing or OCR, or inaccurate parameter mapping in the tool call.

Verification Steps

Upload a bioequivalence report containing complex tables and scanned images. Verify that the file parsing results accurately extract drug names, dosages, and primary pharmacokinetic parameters.
Execute a query involving an external tool call, such as querying production information for a specific drug batch. Verify that the returned results match the actual data in the LIMS system.
For a document containing statistical analysis results, query its 90% confidence interval range. Verify the accuracy of the returned confidence interval values.

The values provided are common starting points. Measure them 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.