The Model Context Protocol (MCP) initially transformed AI agents' interactions with enterprise systems by standardizing tool connections, but scalability led to severe issues with tool selection accuracy, response times, and costs, known as the MCP tool overload problem. This was addressed by shifting tool selection from a reasoning approach to a retrieval-based one, using Redis for Tool Filtering to efficiently retrieve relevant tools through vector or hybrid search, drastically reducing token usage by 98%, speeding up retrieval by 8 times, and doubling accuracy. This method involves generating vector embeddings for each tool's metadata, storing them in Redis, and conducting semantic searches to retrieve only the most relevant tools for a given query, ensuring high performance without the need for new infrastructure. In practical applications, this approach reduced token usage from 23,000 to 450 per request, cut response times from 3.4 seconds to 392 milliseconds, and increased tool selection accuracy from 42% to 85%, highlighting the necessity of intelligent filtering for scalable agent deployments.