Extraction of Human Phenotype Ontology (HPO) Concepts from Clinical Notes Utilizing Large Language Models (LLM) with Model Context Protocol (MCP)

Background: Accurate extraction of Human Phenotype Ontology (HPO) terms from clinical notes is essential for variant prioritization and genetic diagnosis. Large language models (LLMs) often struggle to balance precision, hallucination avoidance, and ontology mapping accuracy, and prior work has shown that retrieval-based grounding can improve performance for individual models. We hypothesized that real-time ontology grounding through external tools would improve these metrics across heterogeneous LLMs, and we evaluated the Model Context Protocol (MCP), a standardized open framework for integrating external tools, as a vendor-agnostic mechanism for delivering such grounding. Methods: Five LLMs (Claude Sonnet 4.5, GPT-5.1, Gemini 2.5 Pro, Grok 4.1, and Qwen3 30B) extracted HPO terms from four synthetic clinical genetics notes under two conditions: baseline ("No Tools," internal knowledge only) and tool-augmented ("With Tools"), with real-time HPO retrieval delivered through MCP for models with native support and through functionally equivalent native tool-calling interfaces otherwise. Each model performed [&ge;]50 runs per note per condition (>2,000 total runs). Performance was evaluated using Precision, Recall, and F1-score. Outputs were manually adjudicated to classify mapping errors and hallucinations. Results were benchmarked against a commercial EHR-based HPO extraction tool. Results: Tool augmentation significantly improved performance across all models. Mean aggregate F1-score increased from 0.46 (SD 0.22) in the baseline condition to 0.72 (SD 0.15) with tools (p < 0.001). Mapping Error Rate decreased from 40.9% to 7.8% (p < 0.001), and Precision increased from 56% to 90%. Performance gains were observed across all model families, including the open-weight Qwen3 model (F1 0.11[-&gt;]0.50). For inferred phenotypes, F1 improved from 0.20 to 0.34 (p < 0.001) without a significant increase in hallucination rate (p = 0.08). Compared with the commercial benchmark, tool-augmented LLMs achieved higher F1-scores and substantially greater recall for inferred phenotypes. Conclusions: Real-time ontology grounding substantially improves HPO extraction across diverse LLMs by reducing mapping errors and enhancing phenotype inference. The Model Context Protocol provides a standardized, interoperable mechanism for delivering such grounding, supporting reproducible, vendor-agnostic deployment of clinical LLM pipelines in genomic medicine.