SemanticDrift: A Research Proposal and Preliminary Analysis of Temporal Behavioral Drift in Model Context Protocol Tool Descriptions

The Model Context Protocol (MCP) has emerged as the dominant standard for connecting AI agents to external tools and services. At the core of MCP is a trust assumption: an agent reads a tool's registered description and acts on it as an accurate contract of the tool's behavior. This paper identifies and formalizes a previously unstudied failure mode called Semantic Drift, defined as the progressive divergence between what an MCP tool's description claims and what the tool demonstrably does at runtime, accumulating across successive version updates. We present a research proposal and preliminary analysis for studying this phenomenon at scale. Three contributions are made: (1) a formal four-category taxonomy of MCP semantic drift covering output-schema drift, capability-scope drift, side-effect drift, and latency-contract drift; (2) the design of SemanticDrift, an automated pipeline that probes live MCP servers with synthetic inputs, compares observed behavior against registered descriptions through an LLM-as-judge evaluation stage, and computes a per-tool Drift Index; and (3) a detailed experimental plan targeting approximately 300 open-source MCP servers across three public registries, with pilot analysis suggesting that a substantial proportion of version updates introduce measurable drift. If confirmed at scale, these findings would reveal a systemic and previously unmeasured reliability and security gap in the MCP ecosystem. This paper is intended as a precursor to a full empirical study and invites collaboration and review of the methodology from the broader community.