KSJ MCP Server

KSJ MCP Server

Knowledge Synthesis Journal v2.0 — AI companion

Turn your handwritten journal photos into a searchable, AI-powered knowledge base — privately, on your own machine.

"Works great on paper. Magical with AI."

Get the journal: Knowledge Synthesis Journal v2.0 on Amazon

What it does

The KSJ MCP server connects your knowledge — handwritten or digital — to an AI assistant via the Model Context Protocol (MCP) — an open standard for linking AI models to local tools and data.

Physical journal → knowledge base

Photograph a journal page, upload it, and your AI assistant can:

• Search across everything you've ever written
• Find connections between ideas (shared tags, @ references)
• Surface your open questions, key insights, and breakthroughs
• Export your knowledge base as Markdown or JSON

AI research sessions → structured insights

Spend an hour going deep on a topic with an AI assistant and most of that thinking vanishes when the chat ends. extract_ai_insights fixes that — paste or pipe a session transcript and the server extracts what matters:

• Novel hypotheses and seed ideas
• Unexpected connections between concepts
• Open questions worth pursuing
• Decisions made and action items

Each insight is confidence-scored (🟢 Seed / 🔴 Developing / 🟡 Strong) and shown to you for review before anything is written to the database. Approved entries are stored alongside your journal captures with full tag support, so AI-extracted insights surface in searches, connection graphs, and synthesis suggestions alongside your handwritten notes.

All processing is local. Your notes stay on your machine.

AI Platform Support

This server uses MCP (Model Context Protocol), an open standard with growing support across AI platforms and developer tools.

Currently supported:

• Claude Desktop (free) — full MCP support, recommended for getting started

Other MCP-compatible clients (Cursor, VS Code + GitHub Copilot, and others) can connect using the same config — check your client's MCP documentation for setup details.

Using ChatGPT, Gemini, or another platform? Use the export_captures tool to dump your knowledge base as Markdown or JSON, then paste it into your AI assistant of choice. Full native MCP support for additional platforms is on the roadmap as the ecosystem grows.

Setup (4 steps)

Step 1 — Install an MCP-compatible AI client

The fastest way to get started is Claude Desktop (free at claude.ai/download).

For other MCP clients, consult their documentation for how to register a local MCP server, then use the config in Step 4.

Step 2 — Install Tesseract OCR

Tesseract reads the text from your journal photos. It must be installed separately.

After installing, restart your AI client so the updated PATH is picked up.

Windows note: If you skip "Add to PATH", the server will still auto-detect Tesseract at the default install location (C:\Program Files\Tesseract-OCR\). Adding to PATH is recommended but not required.

Step 3 — Install uv and the KSJ server

uv is a fast Python package manager used to install and run the KSJ server.

Install uv:

Verify with uv --version in a terminal before continuing.

Install the KSJ server (run once in a terminal):

uv tool install --from git+https://github.com/ChavezAILabs/ksj-mcp ksj-mcp

This installs ksj-mcp as a persistent command on your machine. Git must be installed for this step (Windows: Git for Windows).

Verify with ksj-mcp --help — if it shows a help message, the install worked.

To update later:

uv tool upgrade ksj-mcp

Step 4 — Register the server

Claude Desktop config file location:

Add the following block:

{
  "mcpServers": {
    "ksj": {
      "command": "ksj-mcp"
    }
  }
}

Save and restart your AI client. You should see ksj listed in the tools/integrations panel.

Usage

Once connected, talk to your AI assistant naturally.

Uploading:

"Upload my journal photo from /Users/me/Desktop/RC-001.jpg"

"Process all the photos in my /Desktop/journal-scans folder"

Searching & browsing:

"Search my notes for ideas about spaced re