# Cache Optimization

The MCP Search Server is a protocol service that provides Google search functionality for AI models. It supports caching and advanced search configurations and enables information retrieval through Docker deployment.

A server implementation based on the MCP protocol, providing advanced search functions for files in Microsoft 365 (SharePoint/OneDrive), supporting file content retrieval, metadata analysis, and business workflow integration.

An MCP server implementation based on Gemini, providing powerful context management and caching functions, supporting a context window of up to 2 million tokens, and suitable for integration with various AI clients.

This project uses Docker to build an MCP server that integrates Redis and TimescaleDB, providing features such as fast deployment, automatic restart, and useful scripts.

An MCP service that provides NPM package documentation and metadata query, supporting local caching to improve performance.

A memory cache server based on the MCP protocol that reduces token consumption by efficiently caching language model interaction data, supporting automatic management and configuration optimization.

A server implementation based on the MCP protocol that provides file search functionality for Microsoft 365 (SharePoint/OneDrive), supporting efficient file discovery, metadata analysis, and business process integration.