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NVEIL

Generates local charts and data visualizations from natural-language requests through an MCP-compatible stdio interface.

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Install NVEIL

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "nveil": {
      "command": "uvx",
      "args": [
        "nveil"
      ],
      "env": {
        "NVEIL_API_KEY": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

We couldn’t confirm it starts correctly

The install command installed and started the server, which then stopped for configuration (such as an API key) that our sandbox does not have — expected for servers that connect to your account.

uvx nveil

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1d ago.

Overview

The NVEIL MCP server connects AI clients to NVEIL's natural-language data visualization toolkit. It sends dataset metadata, such as column names, types, and aggregate statistics, to NVEIL AI while keeping raw data on the local machine. The toolkit performs processing and rendering locally, producing charts through Plotly, VTK, or DeckGL. Reach for it when an agent needs reproducible visualizations, local rendering, or reusable chart specifications.

Use cases

  • Generate charts from local CSV or dataframe data
  • Ask an agent to plan joins, pivots, and aggregations
  • Render saved visualization specifications on fresh data
  • Create geospatial, scientific, or 3D visualizations
  • Keep raw datasets on the local machine during visualization

Key features

  • Natural-language chart and data-processing requests
  • Local execution with metadata-only planning
  • Plotly, VTK, and DeckGL rendering backends
  • Reusable .nveil visualization specifications
  • Support for 50+ visualization types
  • MCP stdio server launched with nveil mcp

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 3d ago

Recent health check succeeded.

7/7 checks healthy over the last 47d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 13d ago via OSV.dev · nveil (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars3Stargazers on the source repository.
Last commit4mo agoMost recent push to the default branch.
Install checkRunsOur sandbox started it; it then asked for an API key or config.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How NVEIL works

What NVEIL MCP server does

The NVEIL MCP server exposes NVEIL's data processing and visualization workflow to MCP clients. An agent can describe a desired chart or analysis in natural language, using datasets available to the local environment. NVEIL plans the required operations and visualization, then the toolkit executes those operations locally.

The workflow covers more than basic chart generation. Supported use cases include joins, aggregations, pivots, time-series processing, geospatial heatmaps, 3D volumes, scientific plots, medical imaging, biosignal data, network graphs, and other visualization types. NVEIL supports more than 50 visualization types and can select Plotly for 2D output, VTK for 3D or medical data, and DeckGL for geospatial visualizations.

How it works

The NVEIL MCP server uses a two-stage process. First, NVEIL AI receives dataset metadata rather than the raw records. The documented metadata includes column names, data types, and aggregate statistics. NVEIL AI returns a processing and visualization plan based on the user's request.

Next, the local toolkit carries out the plan. Joins, transformations, aggregations, pivots, and rendering happen on the user's machine. The result can be displayed as a figure or exported in supported formats. This separation is useful when raw data must remain local while an agent still needs help selecting a visualization and planning data operations.

Generated specifications are deterministic and can be saved as .nveil files. A saved specification can later be loaded and rendered against fresh data without another API call, which supports repeatable reports and offline re-rendering.

Setup and configuration

Install the Python package with:

Terminal
pip install nveil

Python 3.10 or newer is required. Create an account at app.nveil.com, generate an API key in Settings, and provide it through the NVEIL_API_KEY environment variable or the Python configuration API.

To launch the MCP interface, run:

bash
nveil mcp

The command starts a stdio server for an MCP client. A client configuration can invoke the nveil executable with mcp as its argument. The README specifically documents use with Claude Code, Claude Desktop, Cursor, and Codex.

Tools and capabilities

The available workflow supports:

  • Natural-language requests for charts and data processing
  • Local execution of joins, aggregations, pivots, and rendering
  • Automatic selection among Plotly, VTK, and DeckGL backends
  • Publication-ready HTML and PNG output through the toolkit's command-line workflow
  • Reusable .nveil specifications that render on new datasets
  • Visualization of 2D, 3D, geospatial, scientific, medical, biosignal, and network data

The MCP interface is a fit for agents that need to inspect or transform local datasets and produce visual output without loading raw records into the remote planning service.

Limitations and notes

An NVEIL API key and account are required for the planning stage. The privacy model keeps raw data local, but metadata is sent to NVEIL AI. The README identifies this metadata as column names, types, and aggregate statistics, so deployments should evaluate whether those details are acceptable for their data.

Rendering a generated specification is local and requires no API call. However, creating a new specification uses the NVEIL planning service. The MCP server is launched over stdio, so the selected MCP client must be able to start a local command and provide access to the datasets used by the request.

NVEIL is licensed under GNU AGPL v3 or later. The project also states that commercial dual licensing is available through the vendor.

Read the full README View source on GitHub

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Frequently Asked Questions about NVEIL

NVEIL is an MCP server that connects AI clients to the NVEIL data visualization toolkit. Its main tools let agents describe datasets, generate visualization specifications, render charts, and work with local data while sending only metadata such as column names, types, and statistics to NVEIL AI. Rendering remains local.

Install NVEIL with `pip install nveil`, then launch its MCP interface with `nveil mcp`. Python 3.10 or newer is required. Configure the MCP client to run the `nveil` command with `mcp` as its argument.

NVEIL uses `NVEIL_API_KEY` for the API key required to create visualization specifications. Create an account at app.nveil.com, generate the key in Settings, and export it before using the toolkit or MCP workflow.

Yes, NVEIL documents Cursor as a supported MCP client. Configure Cursor to launch the local `nveil mcp` stdio command, allowing the client to use NVEIL's natural-language visualization workflow with locally available data.

No, raw data stays on the local machine. NVEIL sends metadata such as column names, data types, and aggregate statistics to plan processing and visualization. Joins, aggregations, transformations, and rendering are performed locally.

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