# Ascent: An Agentic System over the Model Context Protocol for Real-World Clinical Data Analysis

arXiv: https://arxiv.org/abs/2609.24620 · HTML: https://mlsanity.com/paper/2609.24620/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 21 Sep 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 17 of 17)

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- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs](https://mlsanity.com/paper/2406.01297/) (2024)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL](https://mlsanity.com/paper/2411.08599/) (2024)
- [XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL](https://mlsanity.com/paper/2507.04701/) (2025)
- [ReflecTool: Towards Reflection-Aware Tool-Augmented Clinical Agents](https://mlsanity.com/paper/2410.17657/) (2024)
- [Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records](https://mlsanity.com/paper/2403.09226/) (2024)
- [EHR-MCP: Real-world Evaluation of Clinical Information Retrieval by Large Language Models via Model Context Protocol](https://mlsanity.com/paper/2509.15957/) (2025)
- [Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation](https://mlsanity.com/paper/2502.21107/) (2025)
- [Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL](https://mlsanity.com/paper/2601.09876/) (2026)
- [Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Study](https://mlsanity.com/paper/2602.12015/) (2026)
- [CodeClinic: Evaluating Automation of Coding Skills for Clinical Reasoning Agents](https://mlsanity.com/paper/2605.09675/) (2026)
- [Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes](https://mlsanity.com/paper/2507.12261/) (2025)
- [From Conversation to Query Execution: Benchmarking User and Tool Interactions for EHR Database Agents](https://mlsanity.com/paper/2509.23415/) (2025)

## Abstract (arXiv)

Answering epidemiological questions from real-world clinical data requires medical coding, schema-aware SQL, and validation of implicit choices about populations, denominators, and time. We present Ascent, an agentic system that exposes medical coding, question answering, and cohort analysis through a shared Model Context Protocol tool surface for standardized and native schemas. We introduce EpiTrap, a dataset testing whether systems avoid recognized pharmacoepidemiological errors, and compare a fixed pipeline with agents across models and orchestrators. With capable models, agents improve accuracy over the fixed pipeline by an average of 27 and 20 percentage points on native and standardized schemas, respectively. These gains require more tool calls and longer runtimes. Experience from real projects highlights the system's value for feasibility assessment, diagnostic iteration, and expert-guided analysis.