exasol-quickstart 0.3.5

One command to try Exasol with AI add-ons (MCP Server + JSON Tables) — auto-selects the right setup for your OS (Windows / macOS / Linux).

⚡ exasol-quickstart

Run Exasol with AI — in one command.

A full Exasol analytics database, an LLM-ready MCP server, and JSON-native SQL — set up the right way for your OS, in minutes.

📦 PyPI  •  💻 GitHub

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⚡ Get started in one line

pipx run exasol-quickstart

_{Prefer uv?  uvx exasol-quickstart}

Choose the form that fits — try it (runs once, nothing installed) or keep it (installs the command for repeated use):

	with pipx	with uv
▶️ Try it once	pipx run exasol-quickstart	uvx exasol-quickstart
📌 Keep it	pipx install exasol-quickstart && exasol-quickstart	uv tool install exasol-quickstart && exasol-quickstart

Every form detects your OS, provisions Exasol the right way, and prints the endpoints. No flags. No multi-step setup.

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🎁 What you get

                       exasol-quickstart
                              │
                              ▼
        ┌──────────────────────────────────────────────┐
        │   Exasol  (database)        127.0.0.1:8563    │
        │      ▲            ▲                            │
        │      │            │                            │
        │   MCP server   JSON Tables                     │
        │   :4896/mcp    ingest JSON → SQL               │
        └──────────────────────────────────────────────┘

Component	Endpoint	Purpose
🗄️ Exasol (database)	127.0.0.1:8563 — sys / exasol	the Exasol SQL engine
🤖 MCP Server	http://127.0.0.1:4896/mcp	connect Claude / any MCP client to the database
📦 JSON Tables	exasol-quickstart json-tables …	ingest JSON and query it as SQL

_{Web UI: https://127.0.0.1:8443}

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✅ Requirements

The only thing you always need is Python 3.9+ with pipx:

python -m pip install --user pipx
python -m pipx ensurepath        # then reopen the terminal

From there, exasol-quickstart picks how Exasol runs, per platform:

Operating system	How Exasol runs	Docker
🪟 Windows	Exasol Nano, in a container	required (no native Windows engine exists)
🍎 macOS (Apple Silicon)	Exasol Personal, in a native VM	not required (experimental)
🐧 Linux	Exasol Nano, in a container (native install planned)	required for now

The container path is fully tested today; the macOS native path is experimental and not yet validated end to end.

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🛠️ Usage

exasol-quickstart                      # full stack: database + MCP + JSON Tables
exasol-quickstart --no-json-tables     # database + MCP only
exasol-quickstart --dry-run            # print the plan, change nothing
exasol-quickstart --base <name>        # force a base: nano-docker | personal | nano-native
exasol-quickstart json-tables --help   # run the JSON Tables CLI

📥 Ingest some JSON

docker cp data.json exasol-quickstart-json-tables:/workspace/data.json
exasol-quickstart json-tables ingest-and-wrap --input /workspace/data.json --name demo
# then query it:  SELECT * FROM "EJT_DEMO_VIEW"."demo";

On the container path, the first run pulls exasol/nano + exasol/mcp-server and builds the JSON Tables image once (it compiles a small Rust engine — a few minutes). Subsequent runs are fast.

🧹 Stop & remove everything

docker rm -f exasol-quickstart-db exasol-quickstart-mcp exasol-quickstart-json-tables

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🧩 How it works

exasol-quickstart is a single front-door command that detects the platform and assembles the stack:

• With Docker — Exasol Nano (database), the official exasol/mcp-server image, and a JSON Tables sidecar run as containers on a shared network. Tested end to end, including ingest.
• Without Docker — macOS uses Exasol Personal (a native VM); Linux uses a native Nano install (planned). Add-ons run as isolated host environments.

MCP Server and JSON Tables have incompatible pyexasol requirements, so each runs in isolation — a separate container, or a separate host environment — never a shared Python environment.

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📍 Status

0.3.x — the container path (Nano + MCP + JSON Tables) is tested end to end, including ingest. No-Docker native bases are chosen automatically when Docker is absent; the macOS path is experimental.

📦 Install from PyPI  •  Made to make trying Exasol effortless  •  MIT