As organizations adopt the Model Context Protocol (MCP) to connect AI agents to tools and APIs, a new scaling problem is emerging: context bloat. Injecting every available tool schema from dozens of MCP servers into a model’s context window increases latency, inflates token costs, and measurably degrades reasoning quality. Teams that started with a handful of tools and now juggle 50 or more across multiple MCP servers are learning this the hardway — agents get slower, less accurate, and more expensive to run, exactly as the tool catalog needed to make them useful keeps growing.