What does MCP Server Obsidian Omnisearch do?
It provides a search tool that finds notes in your Obsidian vault using the Omnisearch plugin and returns their absolute file paths.
@MCP-Mirror
基本情報
Mirror of
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": {
"anpigon_mcp-server-obsidian-omnisearch": {
"command": "python",
"args": [
"server.py",
"/path/to/your/obsidian/vault"
]
}
}
}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。
MCP Server Obsidian Omnisearch is a FastMCP-based server that exposes Obsidian vault search functionality through a REST API. It lets you programmatically search notes in your Obsidian vault using the Omnisearch plugin, returning absolute file paths of matching notes.
Install dependencies with uv install, then run the server by passing your vault path as a command‑line argument (e.g., python server.py /path/to/your/obsidian/vault). Configure it as an MCP tool in Claude Desktop using the provided claude_desktop_config.json snippet.
It provides a search tool that finds notes in your Obsidian vault using the Omnisearch plugin and returns their absolute file paths.
Python 3.x, an active Obsidian vault with the Omnisearch plugin installed and running, and the FastMCP library.
The vault path is supplied as a command‑line argument when starting the server, for example: `python server.py /path/to/your/obsidian/vault`.
It returns a list of absolute file paths for notes that match the search query.
The server runs over stdio and is designed to be used as an MCP tool (e.g., with Claude Desktop). No authentication is required.
Local MCP server and CLI that keeps a Git-versioned, model-written index of a codebase and its database schema, so coding agents start each task with the whole system in view.

Open-source Python SDK & MCP interface for Priostack Agent Context Network (ACN). Zero-setup, autonomous memory orchestration for AI agents.

Orcha is a unified context layer for AI tools and agents. It stores and indexes and exposes organizational, team, or individual knowledge. Files, structured databases, and connected sources live in one workspace, and age

Search published GPU deployment, rack integration and structured cabling resources; read canonical documents and Leviathan Systems company service scope. Anonymous, read-only Streamable HTTP MCP. No credentials required.
Shared, serverless, conflict-free memory for AI agents: a multi-machine drop-in for the MCP memory knowledge graph.
コメント