Cut AI token costs 95%+ on code exploration. The leading MCP server for precise, symbol-level GitHub code retrieval via tree-sitter AST. Works with Claude Code, Cursor & any MCP client. 313B+ tokens saved.

Token efficient Claude Code full Python rebuild. AI Coding Agent in 230K LoC pure Python. Up to 200X Cost Saving!

The leading, most token-efficient MCP server for documentation exploration and retrieval via structured section indexing

NTK — 基于信息密度路由的多智能体框架。用最少的 token 做最多的事。

苹果 MCP 工具 ✅

AI model that animates any SVG into Lottie with 3-4x fewer tokens than OmniLottie (CVPR 2026). Upload SVG → get animated .lottie. 3B model, runs on 5GB VRAM.

Token-efficient CLI for Atlassian Server/DC (Jira, Confluence, Bitbucket) — drop-in alternative to mcp-atlassian for LLM agents. Compact output, lossless XHTML<->Markdown round-trip, Claude Code + Codex + GitHub Copilot integration.

Token-efficient codebase maps for AI IDEs. Parses Python, TypeScript & more with tree-sitter, builds a ranked dependency graph, and exposes it via MCP to Cursor, VS Code, and Claude Desktop.

⚡ ZeroToken Skill — 让 Agent 用最少的 token 做最准的事。提示词纪律规范：压缩无效上下文、无效解释、无效工具调用、无效输出，准确性不降。

Token-efficient stochastic testing for AI agents. 5-20x cost reduction. 10 framework adapters. Paper: arXiv:2603.02601

LONTAR (Layered Orchestration of Narrow-context Templated Agents & RAG). A token-efficient, multi-persona AI agent methodology for Claude Code: specialized experts (architect, backend, frontend, UI/UX, QA, DevOps), chunked knowledge, and reusable templates. Inspired by Indonesian palm-leaf manuscripts.

Token-efficient CLI output formatting for LLM agents

The Compiler-Aware Operating System Kernel CodeGraph Engine for Token-Efficient Structural Codebase Retrieval

Production-grade AI agent orchestration — DAG context, self-evaluating pipelines, skill bundles with memory. Inspired by MUSE-AutoSkill, UserHarness ToM, 01 Systems, LeJEPA, Attention Residuals.

A programming language designed for AI agents. 76% fewer tokens than Python.

Task-file orchestration helper for coordinating scoped agent work

AI agent-optimized browser CLI — snap + act. Extreme token efficiency for LLM-driven browser automation.

Quality layer for Agent Skills (agentskills.io) — lint, hash, sign, version, eval your SKILL.md files. 10 structural checks, Ed25519 signing, semantic diff, 3-stage eval pipeline. Bidirectional SKILL.md ↔ AIF conversion.

CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs

Drop-in TOON adapter for json-render - fewer LLM tokens, no new language