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This research proposes a safety-bounded gateway translating medical device protocols (IEEE 11073 SDC) into the Model Context Protocol (MCP), ensuring AI agents can query health data without executing dangerous physical operations. By enforcing read-only access and policy-validated dry runs, the system prevents unauthorized device manipulation while maintaining semantic clarity in alarm outputs and structured metadata. The work establishes a critical safety boundary for integrating autonomous language-model agents into regulated healthcare environments where deterministic constraints are non-negotiable.
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A Safety-Bounded SDC-to-MCP Gateway for Medical AI Agents

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.31358v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) provides a common interface through which AI applications discover and use external resources and tools. It allows language-model agents to ground their reasoning in current system state and interact with heterogeneous services. In medical environments, however, exposing device state and action affordances requires deterministic constraints on possible effects. We present an IEEE 11073 Service-Oriented Device Connectivity (SDC)-to-MCP gateway that exposes metrics, alarms, context references, and semantic metadata as read-only resources, while representing selected action affordances as policy-validated dry-run tools. The term safety-bounded denotes a narrow no-execution property: agent-facing requests dispatch no SDC device operation. A Python prototype supports simulated fault and lifecycle experiments, a software-reference protocol path spanning independent Java and Python implementations, deterministic baselines, representation ablations, and multi-model agent evaluation. The results show semantically explicit resource exposure, visible rejection of invalid or outdated state, and preservation of the no-execution boundary across resource, proposal, and authorization paths. Explicit semantic metadata improved conformity to required metric identifiers in structured alarm outputs relative to a generic representation, while retained structured-output failures reveal a distinction between plausible narrative answers and task-compliant machine-readable results.

Abstract:The Model Context Protocol (MCP) provides a common interface through which AI applications discover and use external resources and tools. It allows language-model agents to ground their reasoning in current system state and interact with heterogeneous services. In medical environments, however, exposing device state and action affordances requires deterministic constraints on possible effects. We present an IEEE 11073 Service-Oriented Device Connectivity (SDC)-to-MCP gateway that exposes metrics, alarms, context references, and semantic metadata as read-only resources, while representing selected action affordances as policy-validated dry-run tools. The term safety-bounded denotes a narrow no-execution property: agent-facing requests dispatch no SDC device operation. A Python prototype supports simulated fault and lifecycle experiments, a software-reference protocol path spanning independent Java and Python implementations, deterministic baselines, representation ablations, and multi-model agent evaluation. The results show semantically explicit resource exposure, visible rejection of invalid or outdated state, and preservation of the no-execution boundary across resource, proposal, and authorization paths. Explicit semantic metadata improved conformity to required metric identifiers in structured alarm outputs relative to a generic representation, while retained structured-output failures reveal a distinction between plausible narrative answers and task-compliant machine-readable results.

18 pages. Code: this https URL . Software and evaluation artifacts: this https URL

Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)

arXiv:2609.31358 [cs.DC]

(or arXiv:2609.31358v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2609.31358 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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