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.
MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.
Unified MCP server for managing local model runtimes (Ollama, LM Studio, etc.), enabling provider-agnostic discovery, lifecycle management, hardware-fit checks, and delegated inference.
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Creative Commons Attribution Non Commercial No Derivatives 4.0 International
Local MCP server for token optimization, providing tools to compress code/JSON, optimize prompts, and manage placeholder-based content redaction and hydration to reduce LLM token usage.
Configurable MCP server that lets you define LLM-powered tools via JSON, enabling easy integration of multiple models (GPT, Gemini, Claude, etc.) as MCP tools without writing Python code.
