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logseq-mcp

MCP.Pizza Chef: apw124

logseq-mcp is an MCP server that allows AI agents to interact with a local Logseq graph via its API. It facilitates real-time access and manipulation of Logseq data, enabling integration of AI workflows with personal knowledge management. The server requires Python 3.11+, Logseq API enabled with developer mode and token, and is configured through MCP client settings for seamless AI-driven graph operations.

Use This MCP server To

Query and retrieve notes from a local Logseq graph Update or create Logseq pages via AI commands Integrate Logseq knowledge with AI-driven workflows Automate knowledge graph maintenance and tagging Enable AI agents to navigate and summarize Logseq content Sync AI-generated insights back into Logseq pages

README

Logseq MCP Tools

This project provides a set of Model Context Protocol (MCP) tools that enable AI agents to interact with your local Logseq instance.

Installation

  1. Ensure you have Python 3.11+ installed
  2. Clone this repository
  3. Install dependencies:
    pip install -e .

Setup

  1. Make sure your Logseq has the API enabled.

    • In Logseq, go to Settings > Advanced > Developer mode > Enable Developer mode
    • Then, go to Plugins > Turn on Logseq Developer Plugin
    • Also set an API token in the Advanced settings
    • Restart Logseq
  2. Configure the MCP server in your Cursor MCP configuration file (typically at ~/.cursor/mcp.json):

    {
      "mcpServers": {
        "logseq": {
          "command": "/opt/homebrew/bin/uvx",
          "args": ["logseq-mcp"],
          "env": {
            "LOGSEQ_API_URL": "http://localhost:12315",
            "LOGSEQ_TOKEN": "your-token-here"
          }
        }
      }
    }

OR

  1. Configure Claude Code to use the MCP server with:
claude mcp add
  • Select scope
  • Select Stdio
  • LOGSEQ_API_URL=http://localhost:12315 LOGSEQ_TOKEN=your-token-here /opt/homebrew/bin/uvx logseq-mcp

Using with Cursor and Claude

Adding to Cursor's MCP Tools

  1. Configure the MCP server as shown above in the Setup section

  2. Open Cursor and go to the MCP panel (sidebar)

  3. The Logseq tool should appear in your list of available tools

Using with Claude

When using Claude in Cursor, you'll need to inform it that you have Logseq tools available with a prompt similar to:

"You have access to Logseq tools that can help you interact with my Logseq graph. You can use functions like logseq.get_all_pages(), logseq.get_page(name), logseq.create_page(name), etc."

Available Tools

All tools are available under the logseq namespace:

Pages

  • logseq.get_all_pages: Get a list of all pages in the Logseq graph
  • logseq.get_page: Get a specific page by name
  • logseq.create_page: Create a new page
  • logseq.delete_page: Delete a page and all its blocks

Blocks

  • logseq.get_page_blocks: Get all blocks from a specific page
  • logseq.get_block: Get a specific block by ID
  • logseq.create_block: Create a new block on a page
  • logseq.insert_block: Insert a block as a child of another block
  • logseq.update_block: Update an existing block
  • logseq.move_block: Move a block to a different location
  • logseq.remove_block: Remove a block and all its children
  • logseq.search_blocks: Search for blocks matching a query

Working with Logseq

Journal Pages

Journal pages in Logseq have a specific format and attributes:

  1. Use the format "mmm dth, yyyy" (e.g., "Apr 4th, 2025") when creating or accessing journal pages
  2. Journal pages are automatically formatted by Logseq with proper dating
  3. Journal pages have special attributes that are automatically set by Logseq:
    • journal?: true - Indicates this is a journal page
    • journalDay: YYYYMMDD - The date in numeric format (e.g., 20250404 for April 4, 2025)
  4. Example: await logseq.create_page("Apr 4th, 2025")

Important: You do not need to manually set the journal? or journalDay attributes. Simply creating a page with the proper date format (e.g., "Apr 4th, 2025") will automatically configure it as a journal page with the appropriate attributes.

Block Structure and Formatting

Blocks in Logseq have some important characteristics to understand:

  1. Automatic Bullets: All blocks are automatically rendered as bullet points in the Logseq UI

  2. Page Links: Create links using double brackets: [[Page Name]]

  3. Hierarchical Blocks:

    • Block structure data contains hierarchical information:
      • parent: The parent block's ID
      • level: The indentation level (1 for top-level, 2+ for indented blocks)
      • left: The block to the left (typically the parent for indented blocks)
  4. Block Content: When creating blocks, you can include text formatting:

    • Basic Markdown is supported (bold, italic, etc.)
    • Bullet points within a block may have limited support
    • Multi-line content is supported but may be subject to Logseq's parsing rules
  5. Journal Blocks: Blocks created in journal pages inherit special attributes:

    • journal?: true
    • journalDay: YYYYMMDD - Same as the journal page

Note: Like journal pages, these block attributes are automatically handled by Logseq. You don't need to manually set the journal? or journalDay attributes when creating blocks on journal pages.

Example Usage for Common Tasks

Working with the Cursor agent: When you have Logseq MCP tools configured in Cursor, you can give the agent prompts like:

  • "Create a new page called 'Meeting Notes' with bullet points for today's agenda"
  • "Add today's tasks to my journal page with a 'Tasks' section"
  • "Update today's journal entry with [[Project Plan]], set its child element to 'Completed milestone 1'"
  • "Search my graph for blocks about 'python projects' and organize them on a new page"

The agent will use the appropriate Logseq tools to carry out these operations on your graph.

logseq-mcp FAQ

How do I enable the Logseq API for use with logseq-mcp?
Enable Developer mode in Logseq settings, turn on the Developer Plugin, set an API token, and restart Logseq.
What are the system requirements for running logseq-mcp?
You need Python 3.11 or higher and a local Logseq instance with API enabled.
How do I configure logseq-mcp in my MCP client?
Add the logseq server configuration with command, args, and environment variables (LOGSEQ_API_URL and LOGSEQ_TOKEN) in your MCP client config file.
Can logseq-mcp work with multiple AI providers?
Yes, it is provider-agnostic and works with OpenAI, Claude, Gemini, and others supporting MCP.
Is logseq-mcp secure when accessing my Logseq data?
It uses token-based authentication and runs locally, ensuring secure scoped access to your Logseq graph.
Can I extend logseq-mcp to support custom Logseq plugins?
Yes, since it interfaces via the Logseq API, you can extend functionality by adding compatible plugins or API endpoints.
What programming languages is logseq-mcp compatible with?
It is implemented in Python but can be accessed by any MCP client regardless of language, as long as MCP protocol is followed.
How do I troubleshoot connection issues with logseq-mcp?
Verify API URL and token, ensure Logseq developer mode is enabled, and check server logs for errors.