What are the dependencies for AegnticMCP?
Python 3.9+, Docker & Docker Compose, an OpenAI API key, and optionally an Obsidian vault. Neo4j and Qdrant databases are handled by the setup scripts.
@aegntic
基本情報
AegnticMCP automates the creation and management of MCP servers, ensuring they are stable, adaptable, and intelligent.
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"aegnticMCP": {
"command": "docker",
"args": [
"run",
"-d",
"--name",
"qdrant",
"-p",
"6333:6333",
"-v",
"$(pwd)/data/qdrant:/qdrant/storage",
"qdrant/qdrant:latest"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
AegnticMCP is a Retrieval-Augmented Generation (RAG) system that transforms Obsidian vaults into AI-paired cognitive workflow engines with advanced Graphiti knowledge graph integration. It is built for developers and knowledge workers who want to query their Obsidian notes using Claude Code.
Install via pip (pip install obsidian-elite-rag-mcp) or from source. Run obsidian-elite-rag-cli setup and obsidian-elite-rag-cli start-databases to initialize databases, then ingest your vault with obsidian-elite-rag-cli ingest /path/to/vault. Start the MCP server with obsidian-elite-rag-cli server and configure Claude Code by adding the server entry to ~/.config/claude-code/config.json.
Python 3.9+, Docker & Docker Compose, an OpenAI API key, and optionally an Obsidian vault. Neo4j and Qdrant databases are handled by the setup scripts.
Install from PyPI with `pip install obsidian-elite-rag-mcp` or clone the repository and run `pip install -e .` in the `obsidian-elite-rag` directory.
Start the MCP server using `obsidian-elite-rag-cli server`, then add a server entry to `~/.config/claude-code/config.json` with the command and environment variable `OPENAI_API_KEY`.
The CLI supports general, technical, and research queries. The MCP tools provide `query_rag`, `search_knowledge_graph`, `get_entity_context`, `get_related_entities`, and more.
Retrieval speed under 100ms for context-rich queries, knowledge coverage over 95% recall on domain-specific queries, entity recognition accuracy above 90%, and graph traversal under 50ms up to depth 4.

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