logseq-2-mcp 0.3.1

MCP server for semantic search over Logseq journal entries

Bestie MCP

MCP server for semantic search over Logseq journal entries, backed by PGVector and Amazon Bedrock embeddings.

Requirements

• Python 3.11+
• PostgreSQL with the pgvector extension
• AWS credentials with Bedrock access (Titan Embed v2 + Claude)

Setup

pip install -e .
cp .env.example .env  # edit with your values

Environment variables:

Variable	Default	Description
PGVECTOR_DB_URL	postgresql+psycopg://postgres:postgres@localhost:5432/postgres	Postgres connection string
BEDROCK_AWS_PROFILE	—	AWS profile name (takes precedence over key/secret)
BEDROCK_IAM_ACCESS_KEY	—	AWS access key ID
BEDROCK_IAM_SECRET_KEY	—	AWS secret access key

Transports

Streamable HTTP (recommended — for LibreChat, Claude Code, remote use)

The server runs on port 9999 by default and exposes the MCP endpoint at /mcp.

# direct
python -m logseq_mcp.main

# with options
python -m logseq_mcp.main --host 0.0.0.0 --port 9999

# via env var
MCP_TRANSPORT=http python -m logseq_mcp.main

Claude Code — add to .mcp.json in your project root:

{
  "mcpServers": {
    "logseq": {
      "type": "http",
      "url": "http://localhost:9999/mcp"
    }
  }
}

LibreChat — add to librechat.yaml:

mcpServers:
  logseq:
    type: streamable-http
    url: http://localhost:9999/mcp

stdio (for local Claude Desktop / CLI use)

python -m logseq_mcp.main --transport stdio

# via env var
MCP_TRANSPORT=stdio python -m logseq_mcp.main

Claude Code — add to .mcp.json:

{
  "mcpServers": {
    "logseq": {
      "type": "stdio",
      "command": "python",
      "args": ["-m", "logseq_mcp.main", "--transport", "stdio"],
      "env": {
        "PGVECTOR_DB_URL": "postgresql+psycopg://postgres:postgres@localhost:5432/postgres",
        "BEDROCK_AWS_PROFILE": "your-profile"
      }
    }
  }
}

Docker

Runs streamable HTTP on port 9999 via gunicorn + UvicornWorker (4 workers by default).

docker build -t logseq-mcp .
docker run -p 9999:9999 \
  -e PGVECTOR_DB_URL=postgresql+psycopg://user:pass@host:5432/db \
  -e BEDROCK_IAM_ACCESS_KEY=... \
  -e BEDROCK_IAM_SECRET_KEY=... \
  logseq-mcp

To tune the worker count:

docker run -p 9999:9999 ... logseq-mcp \
  gunicorn logseq_mcp.main:app \
  --worker-class uvicorn.workers.UvicornWorker \
  --workers 2 \
  --bind 0.0.0.0:9999

Health check: GET /health

Tools

search_journal_entries

Semantic search over Logseq journal entries using vector similarity.