An MCP server that exposes token-optimization pipeline functions as tools, enabling MCP-compatible hosts to reduce token usage in requests before they are forwarded to an Anthropic-compatible backend.
A local, zero-cloud MCP server for token and text compression. It provides tools to compress, auto-compress, measure, and decompress text using offline rules, lossless gzip packing, or a local Ollama semantic model.
Local MCP server that uses a local Ollama model to rewrite rough prompt drafts into structured, optimized prompts for paid APIs, saving tokens and improving output quality.
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.
Provides reversible context compression for AI agents, reducing token usage while preserving the ability to retrieve original content, and serves as an MCP server for integration with tools like GitHub Copilot and Claude Code.