Datanotes

Datanotes is the open-source database engine for Markdown: it runs a folder of notes as typed, validated tables that your programs and AI agents query, write and undo over MCP or HTTP, while every row stays a plain file you own.

Documentation: datanotes.dev

Why Datanotes

• Agents can't write garbage. Every write is checked against its table: types, links, unique values and CHECK constraints. A refused write changes nothing. Its answer says which value broke which rule and how to fix it.
• Every change can be undone. The engine journals every write it makes. It records who made it: the agent, its session and its token. undo_migration takes any change back.
• It's still just Markdown. A row is a note, or a section of a pack note, and its front matter keys are the columns. Read it, edit it, grep it or commit it to Git. The engine reads your hand edits and checks them too.
• Extensions. Install tables and MCP tools from a git repository, pinned and confirmed (datanotes extension install).
• Local first. Datanotes is Apache-2.0 and runs on your machine. Nothing leaves it unless you turn that feature on.

When not to use it

Datanotes keeps every row as a file, so it isn't a replacement for SQLite or Postgres. Choose a classic database for:

• Large analytical datasets
• High write throughput
• Many concurrent writers on one folder

Datanotes is comfortable at 10,000 notes. At 50,000 notes, the engine takes about 15 seconds for its first start, and 2 to 3 seconds to restart.

Quick start

You need Node.js 22 or newer. On Windows, use Node.js 24.16 or newer, or 26.1 or newer.

Start the engine on a folder, which becomes your database. It can be empty, or hold notes already:

npx -y datanotes serve --database your_database_folder

Run Datanotes on your notes walks you through your first table.

Connect your agents

Agents reach the engine through its Model Context Protocol (MCP) server, datanotes mcp. It uses the engine that runs on the folder, or starts one. Every client runs the same command:

npx -y datanotes mcp --database your_database_folder

claude mcp add datanotes -- \
  npx -y datanotes mcp --database your_database_folder

Add the server to claude_desktop_config.json:

{
  "mcpServers": {
    "datanotes": {
      "command": "npx",
      "args": [
        "-y", "datanotes", "mcp", "--database", "your_database_folder"
      ]
    }
  }
}

Add the server to .cursor/mcp.json in your project, or to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "datanotes": {
      "command": "npx",
      "args": [
        "-y", "datanotes", "mcp", "--database", "your_database_folder"
      ]
    }
  }
}

Add the server to .vscode/mcp.json in your workspace:

{
  "servers": {
    "datanotes": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y", "datanotes", "mcp", "--database", "your_database_folder"
      ]
    }
  }
}

Add the server to ~/.codex/config.toml:

[mcp_servers.datanotes]
command = "npx"
args = ["-y", "datanotes", "mcp", "--database", "your_database_folder"]

On Windows, clients start npx through cmd /c. For Claude Code, outside Windows Subsystem for Linux (WSL):

claude mcp add datanotes -- cmd /c npx -y datanotes mcp --database C:\Notes

In a JSON file, the command is cmd and the backslashes are escaped:

{
  "mcpServers": {
    "datanotes": {
      "command": "cmd",
      "args": [
        "/c", "npx", "-y", "datanotes", "mcp", "--database", "C:\\Notes"
      ]
    }
  }
}

Connect your agents covers HTTP transport and named tokens.

Use it from TypeScript

Install the package with npm install datanotes. This script finds the engine on your folder, creates a table and queries it:

import { DatanotesClient } from "datanotes/client";
import { localEngine } from "datanotes/local";

const engine = localEngine("your_database_folder");
if (!engine) throw new Error("Start the engine on the folder first");
const db = new DatanotesClient({ url: engine.url, token: engine.token });

const created = await db.call("create_table", {
  name: "film",
  description: "Classic films I watched or want to watch",
  filename: "{title}",
  columns: [
    {
      name: "title",
      type: "string",
      required: true,
      unique: true,
      description: "The film's title, as on its poster",
    },
    {
      name: "minutes",
      type: "integer",
      min: 1,
      description: "Running time in minutes",
    },
  ],
});
if (!created.ok) throw new Error(String(created.value));

await db.call("insert_row", {
  table: "film",
  values: { title: "Nosferatu", minutes: 94 },
});

// Refused: nothing is written, and the answer explains why
const bad = await db.call("insert_row", {
  table: "film",
  values: { title: "Metropolis", minutes: 0 },
});
if (!bad.ok) console.log(bad.value);

const films = await db.call("select_rows", {
  table: "film",
  where: { minutes: { gt: 90 } },
});
console.log(films.value);

Each film is now a note, main/public/film/Nosferatu.md, with the values in its front matter. The JavaScript client page covers the rest of the client.

Learn more

• Documentation
• How it works
• Operations
• Use Datanotes with Obsidian
• Contributing
• Security
• License

License

Apache License 2.0: see LICENSE and NOTICE. Copyright 2026 Davide Fornelli.