pytauargus-mcp

A Model Context Protocol (MCP) server for Tau-Argus, the statistical disclosure control engine. It lets an AI assistant (for example OpenCode, or any other MCP client) work on your data: you point it at a microdata file, it explores the variables, protects the tables and reports what was suppressed and why. Each tool call is one self-contained task with its full result, so a long solve can be backgrounded by the client. It is a thin wrapper over the pytauargus Python API; it re-implements no engine behaviour. Installing it also gives you the pytauargus package and the tauargus CLI.

What it does

Six tools cover the core workflow.

• tauargus_version reports the pytauargus package version and the native engine version.
• tauargus_explore inspects a microdata dataset and returns its variables with their types. It reads .parquet files directly and .asc/.rda files through the engine metadata parser.
• tauargus_build_arb writes a .arb batch file from the dataset description (wraps micro_arb).
• tauargus_run_arb runs a .arb batch and reports the table files it writes.
• tauargus_protect protects one or more tables from microdata in a single call (wraps protect). It returns per-table cell counts, status counts, the unsafe and secondary cells, and all produced file paths.
• tauargus_audit audits the suppressed cells of a table computed by a batch and returns their realised feasibility intervals.

The server runs on the stdio transport.

Install

Python 3.11 to 3.14, on the platforms the pytauargus wheel covers (macOS arm64, Linux x86_64 and aarch64, Windows x86_64). The wheel bundles the native engine and HiGHS, so no compiler, CMake, R or Java is needed.

pip install pytauargus-mcp
# or, as an extra of the engine package:
pip install "pytauargus[mcp]"

To try it without installing anything (requires uv), run it with uvx:

uvx pytauargus-mcp

Run

tauargus-mcp   # starts the server on stdio

The server is a local process that the client starts for you. Point the client at the tauargus-mcp console script. With a pip install it is on the PATH; without one, use uvx pytauargus-mcp instead.

OpenCode. Add a tauargus server to the project or user config:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "servers": {
      "tauargus": {
        "type": "local",
        "command": ["tauargus-mcp"]
      }
    }
  }
}

Claude Desktop. Add a tauargus entry to claude_desktop_config.json (the app opens it from Settings, Developer, Edit Config) and restart it:

{
  "mcpServers": {
    "tauargus": { "command": "tauargus-mcp" }
  }
}

Desktop starts the command without your shell environment; if it does not find the script, use its absolute path (which tauargus-mcp).

Claude Code.

claude mcp add tauargus -- tauargus-mcp

Gemini CLI.

gemini mcp add -s user tauargus tauargus-mcp

or add the same mcpServers entry to ~/.gemini/settings.json.

ChatGPT. Its custom connectors connect to a remote server over HTTPS and cannot start a local one, so this server does not work with ChatGPT.

When the server connects, the client lists its six tools. In OpenCode, /mcps shows ✓ tauargus connected.

Develop (monorepo)

cd mcp
uv sync --extra test
.venv/bin/python -m pytest tests -q

The uv sync step installs the parent package editable and builds its native extension, so the pinned engine checkout's superbuild output (engine/build/lib) must exist first. scripts/sync-venv.sh builds it if needed (it reads the sibling ../tauargus-engine checkout; point TAUARGUS_ENGINE_DIR at it for the mcp venv too, so the editable build uses the same checkout). The tests read their fixtures from $TAUARGUS_ENGINE_DIR/data (default: the sibling checkout).