mcp-pkm-logseq MCP server

A MCP server for interacting with your Logseq Personal Knowledge Management system using custom instructions

Components

Resources

• `logseq://guide` - Initial instructions on how to interact with this knowledge base

Tools

• `get_personal_notes_instructions()` - Get instructions on how to use the personal notes tool
• `get_personal_notes(topics, from_date, to_date)` - Retrieve personal notes from Logseq that are tagged with the specified topics
• `get_todo_list(done, from_date, to_date)` - Retrieve the todo list from Logseq

Configuration

The following environment variables can be configured:

• `LOGSEQ_API_KEY`: API key for authenticating with Logseq (default: "this-is-my-logseq-mcp-token")
• `LOGSEQ_URL`: URL where the Logseq HTTP API is running (default: "http://localhost:12315")

Quickstart

Install

Claude Desktop and Cursor

On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`

On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

Published Servers Configuration

"mcpServers": {
    "mcp-pkm-logseq": {
      "command": "uvx",
      "args": [
        "mcp-pkm-logseq"
      ],
      "env": {
        "LOGSEQ_API_TOKEN": "your-logseq-api-token",
        "LOGSEQ_URL": "http://localhost:12315"
      }
    }
  }

Claude Code

claude mcp add mcp-pkm-logseq uvx mcp-pkm-logseq

Start Logseq server

Logseq's HTTP API is an interface that runs within your desktop Logseq application. When enabled, it starts a local HTTP server (default port 12315) that allows programmatic access to your Logseq knowledge base. The API supports querying pages and blocks, searching content, and potentially modifying content through authenticated requests.

To enable the Logseq HTTP API server:

1. Open Logseq and go to Settings (upper right corner)

2. Navigate to Advanced

3. Enable "Developer mode"

4. Enable "HTTP API Server"

5. Set your API token (this should match the `LOGSEQ_API_KEY` value in the MCP server configuration)

For more detailed instructions, see: https://logseq-copilot.eindex.me/doc/setup

Create MCP PKM Logseq Page

Create a page named "MCP PKM Logseq" in your Logseq graph to serve as the guide for AI assistants. Add the following content:

• Description of your tagging system (e.g., which tags represent projects, areas, resources)
• List of frequently used tags and what topics they cover
• Common workflows you use to organize information
• Naming conventions for pages and blocks
• Instructions on how you prefer information to be retrieved
• Examples of useful topic combinations for searching
• Any context about your personal knowledge management approach

This page will be displayed whenever the AI thinks it needs to understand the user.

Development

Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:

uv sync

2. Build package distributions:

uv build

This will create source and wheel distributions in the `dist/` directory.

3. Publish to PyPI:

uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags:

• Token: `--token` or `UV_PUBLISH_TOKEN`
• Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD`

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging

experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via `npm` with this command:

npx @modelcontextprotocol/inspector uv --directory /Users/ronie/MCP/mcp-pkm-logseq run mcp-pkm-logseq

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

Add Development Servers Configuration to Claude Desktop

"mcpServers": {
  "mcp-pkm-logseq": {
    "command": "uv",
    "args": [
      "--directory",
      "//mcp-pkm-logseq",
      "run",
      "mcp-pkm-logseq"
    ],
    "env": {
      "LOGSEQ_API_TOKEN": "your-logseq-api-token",
      "LOGSEQ_URL": "http://localhost:12315"
    }
  }
}