Open-source MCP server drastically reducing LLM token usage

This open-source MCP server uses content-aware compression and AST code reading to reduce LLM token usage by up to 98%.

Open-Source MCP Server for Drastic LLM Token Reduction

Developers building with Large Language Models (LLMs) face significant costs and latency associated with token usage. Traditional methods often send large amounts of raw data to LLMs, leading to inefficient processing. This open-source MCP server offers a novel solution by drastically reducing the number of tokens required for LLM interactions, making AI development more cost-effective and performant.

What it Does

This MCP server acts as an intermediary between your application and LLMs. It intercepts data destined for the LLM, applies intelligent compression techniques, and then sends the significantly smaller payload. This process leverages advanced content-aware compression and Abstract Syntax Tree (AST) code reading to understand the underlying structure of the data, allowing for highly targeted and effective token reduction. The result is a reduction in LLM token usage by up to 98%, directly translating to lower operational costs and faster response times.

Key Features

Content-Aware Compression: Analyzes data content to apply the most efficient compression algorithms, going beyond generic methods.
AST Code Reading: Utilizes Abstract Syntax Trees to understand code structure, enabling precise token optimization for code-related queries.
Significant Token Reduction: Achieves up to 98% reduction in LLM token usage, minimizing costs and improving performance.
Open-Source: Developed and maintained under an open-source license, providing transparency and community-driven improvements.
GitHub Source: Full source code available on GitHub for review, modification, and integration.

Who it's For

This tool is specifically designed for AI builders, developers, and organizations that are:

Cost-Conscious: Looking to significantly reduce LLM API costs.
Performance-Oriented: Aiming to improve the latency of their LLM-powered applications.
Code-Heavy Workloads: Dealing with LLM interactions involving significant amounts of code, such as code generation, analysis, or refactoring.
Seeking Optimization: Actively seeking advanced techniques to optimize their AI development workflows.