1. 개요 및 개념

knowledge-mcp는 전문 지식 영역과 AI 조수 사이의 간격을 브릿지하도록 설계된 MCP 서버입니다. MCP(Model Context Protocol) 서버 인터페이스를 통해 AI Agent에 접근할 수 있는 정보를 만들고, 관리, 쿼리 전용 지식 베이스를 만들 수 있습니다.

핵심 아이디어는 MCP 클라이언트 (클래드 데스크탑 또는 Windsurf와 같은 IDE와 같은) 인 AI 조수가 이러한 전문 지식베이스를 능동적으로 상담하기 위해 MCP 클라이언트 (클래드 데스크탑 또는 IDE와 같은) 인 반면 사용자의 신속한 또는 넓은 웹 검색에 대한 일반적인 세심한 검색에 의존하는 것보다. 특정 도메인을 처리할 때 더 정확하고 컨텍스트 인식 응답이 가능합니다.

주요 성분:

• CLI 도구: 지식 베이스 관리(creating, deleting, add/removing document, configuring, searching)을 위한 사용자 친화적인 명령줄 인터페이스를 제공합니다.
• Knowledge Base Engine: Leverages LightRAG 문서 처리, embedding, 지식 그래프 작성 및 복잡한 쿼리 처리 처리.

knowledge-mcp: Specialized Knowledge Bases for AI Agents

1. Overview and Concept

knowledge-mcp is a MCP server designed to bridge the gap between specialized knowledge domains and AI assistants. It allows users to create, manage, and query dedicated knowledge bases, making this information accessible to AI agents through an MCP (Model Context Protocol) server interface.

The core idea is to empower AI assistants that are MCP clients (like Claude Desktop or IDEs like Windsurf) to proactively consult these specialized knowledge bases during their reasoning process (Chain of Thought), rather than relying solely on general semantic search against user prompts or broad web searches. This enables more accurate, context-aware responses when dealing with specific domains.

Key components:

• CLI Tool: Provides a user-friendly command-line interface for managing knowledge bases (creating, deleting, adding/removing documents, configuring, searching).
• Knowledge Base Engine: Leverages LightRAG to handle document processing, embedding, knowledge graph creation, and complex querying.
• MCP Server: Exposes the search functionality of the knowledge bases via the FastMCP protocol, allowing compatible AI agents to query them directly.

2. About LightRAG

This project utilizes LightRAG (HKUDS/LightRAG) as its core engine for knowledge base creation and querying. LightRAG is a powerful framework designed to enhance Large Language Models (LLMs) by integrating Retrieval-Augmented Generation (RAG) with knowledge graph techniques.

Key features of LightRAG relevant to this project:

• Document Processing Pipeline: Ingests documents (PDF, Text, Markdown, DOCX), chunks them, extracts entities and relationships using an LLM, and builds both a knowledge graph and vector embeddings.
• Multiple Query Modes: Supports various retrieval strategies (e.g., vector similarity, entity-centric, relationship-focused, hybrid) to find the most relevant context for a given query.
• Flexible Storage: Can use different backends for storing key-value data, vectors, graph information, and document status (this project uses the default file-based storage).
• LLM/Embedding Integration: Supports various providers like OpenAI (used in this project), Ollama, Hugging Face, etc.

By using LightRAG, knowledge-mcp benefits from advanced RAG capabilities that go beyond simple vector search.

3. Installation

Ensure you have Python 3.12 and uv installed.

1. Running the Tool: After installing the package (e.g., using uv pip install -e .), you can run the CLI using uvx:

   # General command structure (use either --config or --base)
   uvx knowledge-mcp --config   [arguments...]
   uvx knowledge-mcp --base   [arguments...]
   
   # Example: Start interactive shell