Logseq MCP Server

Logseq MCP Server

Turn your Logseq graph into memory and workspace for AI agents. A Model Context Protocol server for Logseq with safety-scoped writes, an audit trail in your daily journal, and verified queries exposed as tools. Built on FastMCP (the high-level API of the official mcp package).

Targets the file/Markdown ("OG") version of Logseq — and plain-text files are part of why a graph makes good agent memory: git-syncable, greppable, durable, no lock-in. The newer DB (SQLite) version changed the underlying schema; some methods may behave differently there.

Why

Agents need durable memory, and you already maintain one — your graph. The missing piece is access you can trust: an agent should read broadly and write usefully, but never touch what it shouldn't — and never do anything you can't see. Three design choices make that possible:

• Namespace-scoped writes. Agents write only under their own prefix (byAgent/ by default), plus one deliberately narrow cross-namespace channel that can change nothing but a task's TODO/DOING/DONE marker. Blacklisted pages are hidden and redacted from every read.
• An audit trail in your daily journal. Every successful write appends a line like 22:30 [[byAgent]] wrote [[byAgent/readingList/...]] to today's journal — reviewing your agents' work becomes part of a morning routine you already have.
• Verified queries as tools. Ship known-good Datalog from config as named tools (query_week_plan, …), so agents don't compose datascript by hand and cheaper models stay reliable.

How I use it

I run a small fleet of Claude Code agents with this server on an always-on Mac mini, against my live personal graph:

• Nightly research. A link dropped into the reading list from the phone; at night an agent claims it (status:: researching), reads the article — or shallow-clones and reads the repo — writes a structured summary onto the page and flips it to read.
• Morning brief. At 08:30 a small model assembles a one-page dashboard — what was read overnight, week-plan progress, current NOW/DOING tasks — and sends a single push notification.
• One journal for everyone. The human's tasks and the agents' audit lines interleave in the same daily note:

The pages the researcher writes — properties, summary, relevance — link straight into the rest of the graph:

flowchart LR
    A[AI agents] -- MCP tools --> S[logseq-mcp]
    S -- HTTP API --> L[Logseq graph]
    S -. audit line per write .-> J[daily journal]
    Y((you)) --> L
    Y -- morning review --> J

Requirements

• A running Logseq with the local HTTP API server enabled (Settings → Features → HTTP APIs server, then start it from the 🔌 menu).
• An authorization token created in the HTTP API server settings.

Usage

Claude Code

Local (stdio), token from the environment:

claude mcp add logseq --scope user --env LOGSEQ_API_TOKEN=<YOUR_TOKEN> -- uvx mcp-server-logseq

Or point it at a remote instance over Streamable HTTP (how phone and remote sessions reach a headless host — see Transports):

claude mcp add logseq --scope user --transport http http://<host>:8000/mcp \
  --header "Authorization: Bearer <LOGSEQ_MCP_HTTP_TOKEN>"

Claude Desktop

{
  "mcpServers": {
    "logseq": {
      "command": "uvx",
      "args": ["mcp-server-logseq"],
      "env": {
        "LOGSEQ_API_TOKEN": "<YOUR_TOKEN>",
        "LOGSEQ_API_URL": "http://127.0.0.1:12315"
      }
    }
  }
}

Configuration

Source	Token	URL
Environment	LOGSEQ_API_TOKEN	LOGSEQ_API_URL (default http://localhost:12315)
CLI flag	--api-key	--url

The token is read from the environment or --api-key; it is never stored in code. A .env file is supported (see .env.example).

Config file (optional)

Behaviour beyond the defaults is set in a TOML file — path from LOGSEQ_MCP_CONFIG (default ~/.config/logseq-mcp/config.toml). Custom queries live in EDN files next to it. The server runs fine with no config file (safe read-mostly defaults); see examples/config.toml for a full annotated example.

Section	Key options
[read]	resolve_depth — how deep to expand ((block refs))
[write]	agent_write_prefix (default byAgent), allow_agents_write_any
[search]	files_path — graph folder; set it to use the ripgrep backend
[blacklist]	pages — pages (and subpages) to hide and redact everywhere
[tasks]	allow_status_change — gate for set_task_status
[audit_log]	enabled — log writes to today's journal
[queries.<name>]	a named query: file/inline query, register_as_tool, …

Secrets and the API URL stay in the environment, never in this file.

Transports

By default the server runs over stdio (for Claude Desktop and other local clients). A Streamable HTTP transport is also available for remote/networked use (e.g. a phone client):

LOGSEQ_MCP_HTTP_TOKEN=<client-secret> \
  mcp-server-logseq --transport streamable-http --host 0.0.0.0 --port 8000
# MCP endpoint: http://<host>:8000/mcp

Env vars: LOGSEQ_MCP_TRANSPORT, LOGSEQ_MCP_HOST, LOGSEQ_MCP_PORT, LOGSEQ_MCP_HTTP_TOKEN (or --http-token).

Authentication

The Streamable HTTP transport requires a bearer token: every request must send Authorization: Bearer <LOGSEQ_MCP_HTTP_TOKEN>, or it gets 401. The server refuses to start in this mode without a token set. Note this is a distinct secret from LOGSEQ_API_TOKEN:

Secret	Direction
LOGSEQ_API_TOKEN	this server → Logseq
LOGSEQ_MCP_HTTP_TOKEN	client (phone) → this server

⚠️ A bearer token over plain HTTP is only safe on an already-encrypted channel. Don't expose the raw port to the open internet. The easy path for a home/headless host is Tailscale: install it on the host and the client, and reach http://<host>.<tailnet>.ts.net:8000/mcp over the encrypted tunnel — no domains, nginx, or certificates. (tailscale serve can add TLS if you want https://.)

Docker

Build once:

docker build -t logseq-mcp .

Quick try (ephemeral — --rm removes the container on stop):

docker run --rm -p 8000:8000 \
  -e LOGSEQ_API_TOKEN=<logseq-token> \
  -e LOGSEQ_MCP_HTTP_TOKEN=<client-secret> \
  -e TZ=Europe/Moscow \
  logseq-mcp

Persistent deploy (e.g. a headless Mac mini) — run once; --restart brings it back after reboots:

docker run -d --name logseq-mcp --restart unless-stopped -p 8000:8000 \
  -e LOGSEQ_API_TOKEN=<logseq-token> \
  -e LOGSEQ_MCP_HTTP_TOKEN=<client-secret> \
  -e TZ=Europe/Moscow \
  -e LOGSEQ_MCP_CONFIG=/cfg/config.toml \
  -v /path/to/config-dir:/cfg:ro \
  -v "/path/to/your/graph:/graph:ro" \
  logseq-mcp

• -v .../config-dir:/cfg — folder holding your config.toml (+ queries/, rules/); set files_path = "/graph" in it to enable file search. Omit both the mount and LOGSEQ_MCP_CONFIG to run on defaults.
• -v .../graph:/graph — your Logseq graph folder (read-only), for file search.
• -e TZ=<zone> — local time for audit-log timestamps (image bundles tzdata; the clock is UTC otherwise).

The container serves Streamable HTTP on port 8000 and talks to a Logseq running on the host. On Docker Desktop (macOS/Windows) the default LOGSEQ_API_URL=http://host.docker.internal:12315 already points at the host; on Linux add --add-host=host.docker.internal:host-gateway (or set LOGSEQ_API_URL to the host IP). Make sure Logseq's HTTP API server is running and listening.

Tools

All read output is normalized to a flat JSON shape and passed through the blacklist. Reads resolve ((block refs)) non-lossily (the resolved block's uuid/status is kept so you can act on it).

Find

• search — full-text search over block content (query, regex?, limit?, case_sensitive?, exclude_journals?). Uses ripgrep over files_path when set, else a datascript content match.
• find_tasks — task blocks by markers?, tag?, under_tag? (descendant), page?, priority?, limit?.
• list_pages — page names under a namespace prefix? (depth? limits levels). Discovers a namespace's child pages, which are separate pages a parent's read_page won't show. Structure only, not block content.
• custom_query — run a named query from the config (name, inputs?).
• list_custom_queries — list the configured queries.
• datascript_query — run a raw Datalog query (query, inputs?, rules?).

Guide

• get_logseq_guide — returns the authoritative guide for querying/writing this graph (verified Datalog gotchas: lowercase names, prefix descendants, marker and journal-day types, tags vs refs, read/write scoping). A single source of truth co-located with the server, so agents don't re-derive (and mis-derive) behaviour.

Read

• read_page — a page as a normalized block tree (page, depth?).
• read_block — a block and its children (uuid, depth?).

Write (agent namespace only)

• write_note — create/append/replace a page under agent_write_prefix (subpath, content?, mode?, properties?).
• set_page_properties — set/remove page properties (subpath, properties; a null value removes one).
• edit_block — replace one block's content (uuid, old_content, new_content). Read-before-write is enforced: the edit is rejected unless old_content matches the block's exact current content. Agent namespace only.

Tasks

• create_task — create a task block in the agent namespace (title, agent, project?, marker?, priority?, tags?, plan_page?, blocks_on?, on_page?). The only way to create tasks — write_note rejects content that starts with a task marker.
• set_task_status — change only a task's marker (uuid, status); gated by [tasks].allow_status_change.

Dynamic

• query_<name> — each config query with register_as_tool = true is exposed as its own tool.

Development

git clone https://github.com/dailydaniel/logseq-mcp.git
cd logseq-mcp
cp .env.example .env   # fill in LOGSEQ_API_TOKEN
uv sync
uv run mcp-server-logseq

Inspect with the MCP Inspector:

npx @modelcontextprotocol/inspector uv --directory . run mcp-server-logseq

License

MIT