NVEIL

Name: NVEIL
Author: nveil-ai

@nveil-aiMCP server · External

Data processing and visualization toolkit — 50+ chart types, raw data stays local.

Data visualizationv1.3.0AGPL-3.0Python

Freeno charge per call

How to install

Runs on your machine from its package; the steps are below.

NVEIL multi-panel dashboard with charts, heatmaps, and flow diagrams — From the README · 1 of 2

What it does

Reads
Writes
High impact

At most: only reads or looks up information.

What it costs

Free

Odel charges nothing per call. The publisher may charge for its own service.

Runs locally over stdio

This server isn't hosted — your MCP client launches it from a package registry. Use one of the commands below, or drop the config into your client (e.g. Claude Desktop).

PyPInveilv1.3.0

Run

uvx nveil

MCP client config

{
  "mcpServers": {
    "nveil": {
      "command": "uvx",
      "args": [
        "nveil"
      ]
    }
  }
}

README4 min readView on GitHub

NVEIL Toolkit

Describe your data. Get production charts. Your data stays local.

Quickstart • API Reference • Examples • Changelog

NVEIL is an AI-powered data visualization toolkit. Write one line of natural language, and NVEIL processes your data and generates publication-ready visualizations — no chart code, no hallucinations, no data leaving your machine.

python

import nveil

nveil.configure(api_key="nveil_...")

# Pass a file path directly — no DataFrame loading required.
spec = nveil.generate_spec("Revenue by region, colored by quarter", "sales.csv")

fig = spec.render("sales.csv")   # 100% local — no API call
nveil.show(fig)                   # opens in browser

From your shell

After pip install nveil the nveil command is on your $PATH:

bash

export NVEIL_API_KEY=nveil_...

# Ground yourself on the dataset (shape / dtypes / head preview)
nveil describe sales.csv

# Generate HTML + PNG + a reusable .nveil spec, print the explanation
nveil generate "Revenue by region, colored by quarter" \
  --data sales.csv --format all --explain

# Re-render an existing spec on fresh data — no API call
nveil render chart.nveil --data new_sales.csv

For AI agents (Claude Code / Claude Desktop / Cursor / Codex / …)

NVEIL ships first-class integrations:

bash

# Claude Code / Claude Desktop — install the bundled skill
nveil install-skill

# Claude Desktop, Cursor, any MCP client — add an MCP server:
# {"mcpServers": {"nveil": {"command": "nveil", "args": ["mcp"]}}}
nveil mcp                    # stdio server; launched by the MCP client

NVEIL multi-panel dashboard with charts, heatmaps, and flow diagrams

Why NVEIL?

Capability	NVEIL	Chatbot data analysis¹	LLM-to-viz libraries²	Traditional plotting³
Natural-language input	✓	✓	✓	✗
Raw data stays on your machine	✓	✗	✗	✓
Only schema + stats sent to server	✓	✗	✗	N/A
Deterministic, reproducible output	✓	✗	✗	✓
Offline re-rendering, zero API calls	✓	✗	✗	✓
Portable saved specs (`.nveil` files)	✓	✗	✗	✗
2D + 3D + geospatial + scientific	✓	2D	2D	varies
Multi-backend (Plotly, VTK, DeckGL)	✓	✗	✗	✗
Data processing engine	✓	✓	partial	✗

_{¹ ChatGPT Advanced Data Analysis, Claude Analysis tool, Gemini Data Agent · ² PandasAI, LIDA, Julius, Vanna · ³ Plotly, Matplotlib, Seaborn}

How It Works

Your Data ──> Toolkit ──metadata only──> NVEIL AI ──> Processing Plan ──> Local Execution ──> Result
               ^                                                           ^
          raw data stays here                                     raw data stays here

You describe what you want in plain language
NVEIL AI plans the data processing and visualization (only metadata is sent — column names, types, statistics)
The Toolkit executes locally — joins, aggregations, pivots, rendering — all on your machine
You get a figure — Plotly, VTK, or DeckGL, auto-selected for your data

Key Features

🧠 Two Engines in One

Data processing (joins, pivots, aggregations, geocoding, time series) AND visualization generation from a single prompt.

🔒 Data Privacy by Design

Raw data never leaves your machine. Only column names, types, and aggregate statistics are sent.

📈 Multi-Backend Rendering

Auto-detects the best engine: Plotly (2D charts), VTK (3D/medical), DeckGL (geospatial).

🧪 Auditable Results

⚡ Offline Rendering

spec.render() runs 100% locally with zero API calls.

💾 Reusable Specs

Save to .nveil files, reload later, render on new data — no server needed.

Beyond Simple Charts

NVEIL AI chat — conversational data exploration with geospatial heatmaps

NVEIL handles geospatial heatmaps, 3D volumes, scientific visualizations, medical imaging (DICOM), biosignal data (EDF/EDF+), network graphs, and 50+ other visualization types — all from natural language.

Save Once, Render Forever

python

# Generate once (API call)
spec = nveil.generate_spec("Monthly trend by category", df)
spec.save("trend.nveil")

# Reload anywhere — no API call, no server, no cost
spec = nveil.load_spec("trend.nveil")
fig = spec.render(fresh_data)
nveil.save_image(fig, "report.png")

Installation

bash

pip install nveil

Requirements: Python 3.10+

Getting Started

Create an account at app.nveil.com
Generate an API key in Settings
Start visualizing

python

import os
import nveil

nveil.configure(api_key=os.environ["NVEIL_API_KEY"])

spec = nveil.generate_spec("scatter plot of price vs area", df)
fig = spec.render(df)
nveil.show(fig)

See the examples/ directory for more usage patterns.