Tool Calling and Plugins for Bispecific Antibody Clinical

Data Characteristics

Bispecific antibody (BsAb) clinical trial data is highly specialized and complex. Data sources include global clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), internal pharmaceutical company research reports, and specialized biomedical databases (e.g., PDB, DrugBank). Data update frequencies vary; registries may update weekly, while internal company data updates according to project progress. Document structures typically include detailed trial protocols, patient recruitment criteria, drug mechanisms of action, dose escalation data, pharmacokinetic (PK)/pharmacodynamic (PD) data, and adverse event (AE) reports. Fields are highly specific, such as Target_Antigen_1, Target_Antigen_2, Fc_Region_Engineering, Half_Life_Extension_Strategy, and specific dosage units like mg/kg or nM. The data often contains extensive biomacromolecule structural information and sequence data.

Constraints on Tool Calling and Plugins

The specialized nature of BsAb data imposes high demands on tool calling. Its complex structure and vast information require robust parsing capabilities, for example, extracting key target information or mechanism of action descriptions from unstructured text. Heterogeneous data from multiple sources leads to significant data integration challenges. Different sources may have inconsistent field names, units, and data formats, requiring pre-processing and standardization. The rapid iteration of BsAbs and the continuous emergence of new targets necessitate frequent updates to external tools' and plugins' knowledge bases and data interfaces. Queries involving biomacromolecule sequence and structural data require calling specialized bioinformatics tools. These tools may have longer API response times, posing challenges for timeout settings and asynchronous processing. Accurately identifying BsAb-specific terminology and avoiding ambiguity is crucial for ensuring the accuracy of tool calling results.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`4000 tokens`	Accommodates complex trial protocol descriptions and key biological information, while leaving sufficient space for output.
`PARSE_FILE_TIMEOUT_SECONDS`	`300`	Handles parsing large PDF documents or reports with many figures, preventing file processing failures due to timeouts.
`Recall Count`	`10`	Balances recall rate and processing efficiency, ensuring coverage of most relevant clinical trial information.
`Similarity Threshold`	`0.75`	Increases matching precision for specialized terms and complex concepts, reducing irrelevant results.
`Reranked Return Count`	`3`	Focuses on the most relevant few results, facilitating quick screening and decision-making for engineers.
`API_RETRY_COUNT`	`3`	Addresses occasional network fluctuations or temporary unavailability of external bioinformatics tools or database interfaces.

Common Pitfalls

A tool call returns an empty result. This may occur if the field names returned by the data source API do not match the preset parsing paths, preventing correct information extraction.
In streaming responses, the thought process is not directly outputted. This happens if the stream_output parameter for tool calling is not set to true, or if intermediate steps in the workflow do not pass thought content to subsequent streaming output components.
A variable update tool fails to record the number of calls for a specific classification problem as expected. This is due to incorrect configuration of the trigger conditions or update logic for variable updates in the workflow, or incorrect variable scope settings.

Verification Steps

Perform a simulated query. Check if the returned results include expected key fields like Target_Antigen_1 and Target_Antigen_2. Verify their data types and units.
Upload a PDF document containing a complex trial protocol. Observe if the file parses successfully under the PARSE_FILE_TIMEOUT_SECONDS setting. Check the completeness of the parsed text content.
Trigger a workflow that includes an external API call. Check the logs for API_RETRY_COUNT retry records and confirm if data was successfully retrieved.
Conduct multi-turn dialogue tests. Verify that the model correctly understands and associates historical dialogue information with new queries under the maxContext configuration.

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.