#token-optimization

MCP server for intelligent code context — 87%+ token savings, zero resource overhead. An MCP context engine built on AST call graphs + BM25 ranking, delivering precise code context retrieval for AI coding assistants. No GPU, no external services, pure Python, ready to use out of the box.

Context Minifier & State Guard — Local-first MCP middleware proxy that reduces token waste by 61%, prevents context rot, and adds security hardening

Structural codebase MCP server that exposes symbol-level queries as tools, allowing Claude Code to navigate large codebases without reading entire files. Provides 50+ tools covering dependency graphs, git diffs, checkpoint/rollback, dead code detection, and complexity analysis, with reported 87% token reduction on large projects.

AI-powered code analysis and context optimization tools. Slim large code files to relevant sections (save 60-80% tokens), compress text, analyze conversations for noise, and search past chat knowledge bases. Supports x402 micropayments on Base.

Token-efficient web browser for LLM agents. Extract clean markdown from any public web page — saves 90-95% of tokens compared to raw HTML.

Reduce LLM token usage by 50-70% when sending structured data. TOON (Token-Oriented Object Notation) converts JSON, CSV, XML, YAML, and HTML tables to a compact header-based format. Save $489-$9,789 per 1M requests depending on model.

Discover the power of the “CLGateway”, an the open-source Claude MCP Server Gateway (available on GitHub). Built for users of Claude Desktop, Claude API or Claude Code, this Python-based gateway smartly manages your MCP (Model Context Protocol) servers and dramatically slashes token usage, cutting MCP context bloat by roughly 95%. Rather than forcing Claude to load every tool from all configured MCP servers (often thousands of tokens wasted upfront), the gateway exposes just three core tools initially. When you need a specific MCP server (GitHub, Google Workspace, DataForSEO, etc.), the gateway dynamically loads only what’s required, reducing tokens used, improving context clarity, and boosting performance.