Universal Agent Context System

Example Use Cases

• 1

  Unified context management and semantic conversation tracking across multiple AI platforms.

• 2

  Reduce token usage and costs through smart context compression and deduplication.

• 3

  Persistent memory and package management for skills across sessions and tools.

Description

The Universal Agent Context System (UACS) MCP server acts as middleware to unify and manage AI agent contexts across multiple platforms like Claude, Cursor, Windsurf, and Cline. It offers semantic APIs for structured conversation tracking, automatic knowledge extraction, natural language search, and smart context compression to reduce token costs. UACS enables package management for skills and persistent memory, improving agent recall and reducing manual sync errors, all accessible via Python, CLI, or MCP server integration.