BloodHound-MCP

BloodHound‑MCP: A Blood Flow Dynamics Server for AI Assistants

BloodHound‑MCP is a Model Context Protocol server designed to expose realistic blood flow simulation and analysis capabilities to AI assistants such as Claude. By turning complex cardiovascular physics into a set of programmable tools, the server lets developers query and manipulate hemodynamic data without leaving their AI‑driven workflow. This bridges the gap between medical research, engineering simulation, and conversational AI by providing a unified API that returns interpretable results like velocity fields, pressure maps, or flow‑rate statistics.

The core value of BloodHound‑MCP lies in its ability to deliver real‑time, customizable simulations directly through the MCP interface. Developers can request a new simulation with specific anatomical parameters—such as vessel diameter, blood viscosity, or pulsatile flow conditions—and receive a detailed report of the resulting velocity distribution. Because the server exposes these calculations as simple, typed resources, an AI assistant can ask for “What would happen if the carotid artery diameter is reduced by 20 %?” and instantly obtain a quantitative answer, complete with visual export options.

Key features of the server include:

• Simulation Engine – Runs high‑fidelity computational fluid dynamics models tailored to human vasculature.
• Data Analysis Toolkit – Offers statistical summaries, anomaly detection, and trend extraction from raw simulation outputs.
• Export & Visualization – Supports multiple formats (CSV, JSON, PNG) and can generate on‑the‑fly plots for quick inspection.
• Customizable Parameters – Users can tweak physical constants, boundary conditions, or mesh resolutions to match their experimental setup.
• Extensible Prompt Templates – Pre‑built prompts guide AI assistants in framing queries, interpreting results, and generating reports.

In practice, BloodHound‑MCP enables a range of real‑world scenarios. A medical researcher can prototype hypotheses about disease progression by simulating altered flow patterns in aneurysms. An engineering team might integrate the server into a design pipeline to assess how changes in stent geometry affect perfusion. Educators can use the interactive API to demonstrate cardiovascular principles in a classroom setting, letting students pose questions and see immediate visual feedback.

Integration with AI workflows is straightforward: the server exposes its capabilities through standard MCP endpoints, allowing any compliant client to list available tools, invoke simulations, and receive structured responses. This seamless connection means that an AI assistant can act as a virtual laboratory, orchestrating experiments, interpreting data, and even suggesting next steps—all without manual intervention. The result is a powerful, developer‑friendly platform that democratizes access to sophisticated blood flow analysis and unlocks new possibilities for research, product development, and education.