Charly Memory Cache
Caches data between language model queries to optimize token consumption and reduce costs in AI interactions.
About
This memory cache server, developed for the Model Context Protocol (MCP), optimizes token consumption in AI interactions by efficiently caching data between language model queries. Built with TypeScript and leveraging the MCP SDK, it provides an in-memory storage system with features like LRU eviction, TTL management, and memory usage tracking. The implementation stands out by offering automatic caching of file contents, computation results, and frequently accessed data, reducing the need for repeated token-heavy operations. By integrating seamlessly with any MCP client and language model, this server enables significant performance improvements and cost savings in scenarios involving repetitive data access or computations, making it particularly valuable for applications in data analysis, file processing, and AI-assisted development workflows.
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