KSJ MCP Server

KSJ MCP Server

Converts handwritten journal photos into a searchable, AI-powered local knowledge base using Tesseract OCR. It enables users to query their notes, track insights, and export data through MCP-compatible AI assistants while maintaining complete local data privacy.

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README

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."


What it does

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

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

All processing is local. No cloud. No subscription. 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 (3 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 3.

Step 2 — Install Tesseract OCR

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

Platform Command
Windows Download the installer from UB-Mannheim/tesseract — check "Add to PATH" during install
macOS brew install tesseract
Linux sudo apt install tesseract-ocr

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

Step 3 — Register the server

Claude Desktop config file location:

Platform Path
Windows %APPDATA%\Claude\claude_desktop_config.json
macOS/Linux ~/.config/claude/claude_desktop_config.json

Add the following block (copy exactly — no path to set):

{
  "mcpServers": {
    "ksj": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/ChavezAILabs/ksj-mcp",
        "ksj-mcp"
      ]
    }
  }
}

uvx downloads and runs the server automatically — nothing else to install.

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:

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

"Search my notes for ideas about spaced repetition"

"What are my open questions about machine learning?"

"Show me everything connected to RC-015"

"Export all captures tagged #ai as Markdown"

"How many insights have I captured this month?"


Available tools

Tool What it does
upload_capture OCR a journal photo, parse the template, store it, highlight strongest connection
bulk_upload Process a whole folder of photos at once
search_captures Full-text search with optional tag and date filters
find_connections Show tag-overlap and @-reference connections for a capture
get_stats Overview: counts, top tags, open questions, insights, date range
export_captures Dump your knowledge base as Markdown or JSON
suggest_synthesis Find RC topic clusters ready to become a SYN entry
export_study_deck Export ? questions as a portable CSV study deck (Anki, Quizlet, Notion, etc.)
journal_health KPI dashboard + coaching: velocity, synthesis ratio, review cadence, open questions

Schema tag system

Use these prefixes anywhere on your journal pages — the server extracts them automatically:

Prefix Meaning Example
# Topic / domain #machine-learning
@ Source / reference @RC-012
! Priority / urgency !deadline
? Open question ?why-does-this-work
$ Key insight $breakthrough
A→B Cause / effect study→retention

Troubleshooting

"Tesseract OCR is not installed" Install Tesseract (Step 2 above) and restart your AI client.

"Could not detect a template ID" Make sure the template number (RC-001, SYN-001, etc.) is clearly visible in the photo. Try better lighting or a closer shot.

Server not appearing in tools panel Check that uv is installed (uv --version in a terminal) and that the path in your config file is correct. On Windows, use forward slashes or escaped backslashes in the JSON.


Data location

All your captures are stored locally at:

ksj-mcp/data/captures.db    (SQLite database)
ksj-mcp/data/images/         (image copies, if saved)

The data/ directory is .gitignored and never leaves your machine.


License

MIT — free to use, modify, and share.

Created by Chavez AI Labs LLC paul@chavezailabs.com "Personal knowledge operating system for the AI age"

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