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 LLM quantization. Compress any HuggingFace model to GGUF, GPTQ, or AWQ format. 6 tools: info, check, recommend, quantize, evaluate, push. Self-contained Python server — no external CLI needed.
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.
Advanced Token-Optimized Object Notation MCP server that compresses JSON with up to 85% token reduction using AI-powered pattern detection, providing lossless compression and decompression through 12 MCP tools.
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.
MCP server for semantic code indexing using vector embeddings, enabling AI agents to maintain persistent memory of codebases through natural language queries and intelligent chunking.
