Tool-space interference in the MCP era (www.microsoft.com)

This year’s surge in agentic AI—systems that autonomously conduct research, write code, and manage complex tasks—has largely depended on tightly integrated tool-agent ecosystems designed for seamless cooperation. However, as diverse agents from multiple providers begin to interact within a shared environment, new challenges arise. The Model Context Protocol (MCP), which standardizes how agents access thousands of interoperable tools across platforms like Zapier, Hugging Face, and Shopify, exemplifies this shift toward horizontal integration. While MCP broadens AI capabilities by enabling cross-provider tool use, it also exposes “tool-space interference”—scenarios where overlapping or conflicting tools degrade overall system performance, causing longer task sequences, increased costs, or outright failures. A comprehensive survey of 1,470 MCP servers revealed key technical hurdles contributing to interference. Large tool catalogs often overwhelm AI models, with performance dropping by up to 85% when handling more than 20 tools. Additionally, some tool responses are orders of magnitude larger than typical model context windows, straining memory and inference speed. Complex, deeply nested parameter schemas further hinder efficient tool invocation. Naming collisions and semantic ambiguities worsen coordination, as identical or similar tool names across servers confuse agents, complicating task execution. Error reporting inconsistencies also limit recovery from failures. To address these issues, the researchers developed the open-source MCP Interviewer tool to automatically assess server usability and encourage best practices. This work highlights the evolving pains of scaling AI agent ecosystems and underscores the critical need for improved standards and tooling to realize robust multi-agent cooperation in the MCP era.