Law-MCP: A Legal Compliance MCP Framework

Qianyu Wang

Keywords: Large Language Models, AI Agents, Model Context Protocol (MCP), Intellectual Property Compliance, Automated Legal Framework

Paper Type: Short papers / work-in-progress

TL;DR: We propose Law-MCP, a modular framework that automates intellectual property risk detection and legal compliance for AI agents operating within the Model Context Protocol (MCP) ecosystem.

Abstract: LLMs are moving from passive information processors to agentic systems that can act through the Model Context Protocol (MCP). This shift widens the scope of AI use. At the same time, the rapid growth of the MCP ecosystem creates legal risks. The main risks concern intellectual property (IP) in fringement and the use of data by unverified third-party tools. In this complex supply chain, users face severe information asymmetry and may bear direct liability for outputs produced by opaque components that they do not control. Post-hoc legal remedies are not enough. We propose Law-MCP to build the Bridge between Artificial Intelligence and Law. We analyze how legal risks are distributed across tool calls and user interactions and show the need for technical controls. We then present a MCP framework with modular design with three layers: (1) an MCP Context Aggregation Layer that standardizes data and tracks provenance; (2) a plug-and-play IP Risk Detection Layer that uses a central scheduler to coordinate detection plugins; and (3) a Localized Legal Alert Layer that links technical risks to the laws of specific jurisdictions and to case law. The framework automates IP risk detection to protect users. It also offers developers a verifiable way to show due care, closing the gap between legal duties and agent capabilities.

Submission Number: 45