MCP server that minimizes LLM token usage by compressing, summarizing, filtering, chunk-referencing, and pruning large context before it reaches the model, with heuristic or local-SLM smart actions, caching, and token counting.
MCP server for OpenSearch that enables AI assistants to interact with OpenSearch clusters through a standardized interface for search, index management, and cluster operations.
A Model Context Protocol server implementation that enables natural language interactions with OpenSearch clusters, allowing users to search documents, analyze indices, and manage clusters through simple conversational commands.
A meta-server that aggregates multiple MCP servers into a single interface, reducing token usage by 98%+ through progressive tool discovery and direct code execution that processes data between tools without consuming context window space.
MCP server that reduces AI agent token usage by up to 90% through intelligent context compression. Enables efficient code exploration, multi-file refactoring, and debugging by providing tools for smart reading, searching, and managing code context.
