EEG Server

The Multimodal Medical MCP Servers collection addresses a critical gap in AI‑driven healthcare: the need for real‑time, multimodal data ingestion and processing within a standardized Model Context Protocol framework. Traditional medical AI pipelines often rely on bespoke integrations that require significant engineering effort, limiting the speed at which new modalities can be added or existing ones updated. By exposing a single, well‑defined MCP interface, these servers allow AI assistants—such as Claude—to request and receive processed medical signals without the assistant needing to understand the intricacies of each data source.

At its core, the EEG server demonstrates how raw electroencephalography recordings can be transformed into actionable insights. The server parses continuous EEG streams, applies filtering and artifact rejection, and outputs features like power spectral densities or event‑related potentials. Developers can then expose these processed results as MCP resources, enabling assistants to query “What is the alpha band power at electrode Fz?” or “Show me the latest seizure detection probability.” This level of abstraction lets clinicians and researchers focus on clinical questions rather than data plumbing.

Key capabilities include:

• Modular resource definition – each medical modality (EEG, ECG, imaging) can be packaged as its own MCP server with a clear set of endpoints.
• Tool integration – the server can expose specialized tools (e.g., artifact removal algorithms) that assistants can invoke on demand.
• Prompt customization – developers can provide context‑specific prompts so the assistant tailors its responses to clinical protocols.
• Sampling control – real‑time data streams can be throttled or batched to match the assistant’s processing limits.

Real‑world use cases span from bedside monitoring—where an assistant can alert nurses to abnormal heart rhythms—to research settings, where investigators can query longitudinal EEG trends across cohorts. In each scenario, the MCP server serves as a reliable bridge between raw sensor data and the conversational AI layer, ensuring that information is both accurate and timely.

Integration into existing AI workflows is straightforward: once a server’s configuration is added to the file, the assistant automatically launches it on demand. The server then registers its resources and tools with the MCP runtime, making them discoverable through standard discovery APIs. Developers benefit from a single point of maintenance per modality, reducing duplication and accelerating feature roll‑outs.

What sets these servers apart is their commitment to clinical fidelity. By leveraging proven scientific libraries (e.g., MNE for EEG) and exposing them through MCP, the solution guarantees that the assistant’s outputs are grounded in validated signal‑processing pipelines. This combination of standardization, modularity, and domain expertise makes the Multimodal Medical MCP Servers an indispensable asset for any team looking to embed AI into healthcare workflows.