Quick Start

Starting LINZA needs three things: the package, a folder, and an embedding model.

Install

python -m pip install linza-mcp

If you want LINZA to extract PDF text directly:

python -m pip install "linza-mcp[pdf]"

The normal install is enough for Markdown, TXT, JSON, DOCX, and XLSX. [pdf] adds a local PDF extractor.

Folder

LINZA works with any Markdown folder: an Obsidian vault, project documentation, a research folder, or a standalone working directory.

In the examples below, replace /absolute/path/to/your-folder with your own path.

Embeddings

Semantic search and topic maps need an embedding model. The simplest local path is LM Studio:

1. Open LM Studio.
2. Download an embedding model, for example text-embedding-granite-embedding-278m-multilingual, nomic-embed-text-v1.5, or another multilingual embedding model.
3. Start Local Server.
4. Make sure the endpoint is available at http://127.0.0.1:1234/v1.

MCP Configuration

Claude Desktop, Cursor, OpenCode, and other MCP clients usually use mcpServers:

{
  "mcpServers": {
    "linza": {
      "command": "linza-mcp",
      "env": {
        "LINZA_VAULT": "/absolute/path/to/your-folder",
        "LINZA_EMBED_PROVIDER": "lmstudio",
        "LINZA_EMBED_URL": "http://127.0.0.1:1234/v1",
        "LINZA_EMBED_MODEL": "your-embedding-model-name",
        "LINZA_TOOL_SURFACE": "default"
      }
    }
  }
}

VS Code / Copilot MCP uses servers:

{
  "servers": {
    "linza": {
      "type": "stdio",
      "command": "linza-mcp",
      "env": {
        "LINZA_VAULT": "/absolute/path/to/your-folder",
        "LINZA_EMBED_PROVIDER": "lmstudio",
        "LINZA_EMBED_URL": "http://127.0.0.1:1234/v1",
        "LINZA_EMBED_MODEL": "your-embedding-model-name"
      }
    }
  }
}

Check

linza-mcp --version

Then ask the agent:

Check LINZA with agent_workspace(action="doctor").
Index the folder and show the first 3-5 review cards.

A good first run should stay small: diagnostic, index, map, a few cards. Do not start with mass apply.