MCP Solves the Plug, Not the Trust Boundary (vectoralix.com)

The Model Context Protocol (MCP) was designed to standardize how AI applications interact with various external tools and data sources, streamlining the integration process. However, once applications connect to multiple resources, the challenge shifts from simply accessing tools to managing their selection. As an AI model is exposed to a growing number of tools—potentially up to hundreds—the context window can become saturated with definitions, making it difficult for the model to identify the most relevant tool for the task at hand. This “context tax” not only consumes valuable tokens, limiting the space for actual conversation but also increases the risk of selecting the wrong tool due to semantic similarities. The significance of this limitation is highlighted by Anthropic's workaround, which utilizes a tool search/deferred-tools pattern to address the need for relevant tool discovery without overwhelming the model with excess information. The article argues that MCP's current framework focuses on tool availability rather than their relevance, which invites inconsistencies as different vendors implement their own solutions for tool selection. The call to action is clear: a need for a refined protocol that offers a relevance-aware tool discovery mechanism ensuring that models can efficiently access only the pertinent tools when needed.