How does MCPX help save LLM tokens?

MCPX significantly saves LLM tokens by exposing only three core functions (`describe`, `call`, `resources`) to AI agents, dramatically reducing initial context. It also automatically compresses verbose JSON Schemas into concise TypeScript types.

What core functions does MCPX expose to AI agents?

MCPX exposes three core functions: `describe` (to query available tools and their schemas), `call` (to execute any MCP tool), and `resources` (to list or read MCP server resources).

Does MCPX support different communication protocols?

Yes, MCPX supports multiple transport protocols, including stdio, Streamable HTTP, and SSE, with automatic detection to ensure compatibility with various MCP server types.

Is MCPX suitable for specific AI agents like Claude Code?

Absolutely. MCPX is designed to optimize interaction for LLM Agents like Claude Code, Cursor, and Windsurf, providing a unified and efficient way to manage a diverse set of MCP tools.

MCPX

Name: MCPX
Author: quan2005

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Streamlines AI agent interaction with multiple Model Context Protocol (MCP) servers by dynamically loading and consolidating numerous tools into a concise, on-demand set.

MCPX

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Streamlines AI agent interaction with multiple Model Context Protocol (MCP) servers by dynamically loading and consolidating numerous tools into a concise, on-demand set.

概要

MCPX addresses the challenge of AI agents being inundated with extensive schema payloads when connected to numerous MCP servers. It acts as an intelligent proxy, transforming a multitude of individual MCP tools into just three core functions: `describe` for tool discovery, `call` for execution, and `resources` for managing server assets. This intelligent consolidation drastically reduces the initial context window space required for tool definitions, optimizes token usage through advanced schema-to-TypeScript compression, and employs TOON compression for efficient response data handling, enabling AI agents to operate more effectively and focus on critical tasks.

主な機能

On-demand tool loading, exposing only three core functions (`describe`, `call`, `resources`) to AI agents.
Automatic compression of JSON Schemas into concise TypeScript types to save tokens.
TOON compression for efficient response data transmission.
Support for multiple transport protocols: stdio, Streamable HTTP, and SSE with automatic detection.
Session isolation for each request to prevent state contamination across AI interactions.
1 GitHub stars

ユースケース

Optimizing AI agent performance and token usage when integrating with a large number of MCP servers.
Providing a unified and simplified interface for AI agents to access a diverse ecosystem of MCP tools.
Reducing the initial context burden on LLMs by dynamically presenting tool capabilities only when needed.

概要

主な機能

On-demand tool loading, exposing only three core functions (`describe`, `call`, `resources`) to AI agents.
Automatic compression of JSON Schemas into concise TypeScript types to save tokens.
TOON compression for efficient response data transmission.
Support for multiple transport protocols: stdio, Streamable HTTP, and SSE with automatic detection.
Session isolation for each request to prevent state contamination across AI interactions.
1 GitHub stars

ユースケース

Optimizing AI agent performance and token usage when integrating with a large number of MCP servers.
Providing a unified and simplified interface for AI agents to access a diverse ecosystem of MCP tools.
Reducing the initial context burden on LLMs by dynamically presenting tool capabilities only when needed.