title: TabICL MCP emoji: 🤖 colorFrom: blue colorTo: green sdk: docker app_port: 7860 pinned: false

TabICL MCP Server

State-of-the-art tabular ML inside Claude, ChatGPT, or Gemini — upload a CSV, ask a question, get predictions, explanations, and a report. Free and self-hostable.

TabICL (Inria, ICML 2025/2026) is a tabular foundation model: it classifies and regresses on tabular data without any hyperparameter tuning, via in-context learning. This MCP server makes it usable from natural language in any MCP-compatible assistant or agent.

You:    Here's my customer spreadsheet — who is likely to churn, and why?
Claude: [load_data]  → 2,340 rows, 12 columns, target "churned" looks binary
        [evaluate]   → 89% accuracy, 0.93 ROC-AUC on held-out data
        [explain]    → top drivers: contract_type, tenure, monthly_charges
        [create_report] → here's a shareable HTML report with the details…

Tools

Data limits: ~10k rows pasted inline, ~50k via URL or file. TabICLv2 handles 2–100 features natively.

Use it

Option A — Local (free, private, fastest)

Requires Python ≥ 3.10. With uv: nothing to install, clients run uvx tabicl-mcp.

Claude Desktop — ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "tabicl": { "command": "uvx", "args": ["tabicl-mcp"] }
  }
}

Claude Code: claude mcp add tabicl -- uvx tabicl-mcp

Cursor / VS Code (.vscode/mcp.json or Cursor Settings → MCP):

{
  "servers": {
    "tabicl": { "type": "stdio", "command": "uvx", "args": ["tabicl-mcp"] }
  }
}

(Or pip install tabicl-mcp and use "command": "tabicl-mcp".)

With a local server, load_data accepts file paths — no pasting needed, and no data ever leaves your machine.

Option B — Remote URL (free, for claude.ai / ChatGPT / Gemini)

Deploy your own free endpoint on HuggingFace Spaces:

1. Create a Space → Docker SDK → CPU basic (free).
2. Push this repo to it (the Dockerfile pre-downloads model checkpoints at build).
3. Your MCP endpoint: https://YOUR-SPACE.hf.space/mcp

Then connect:

• claude.ai — Settings → Connectors → Add custom connector → paste the URL.
• ChatGPT — enable Developer Mode (Settings, paid plans) → Apps & Connectors → add the URL.
• Gemini CLI / API — add the URL as a remote MCP server in your config.

Free Spaces sleep after inactivity — the first request after idle takes a minute or two while the Space wakes. Everything after that is fast.

Option C — Docker anywhere

docker build -t tabicl-mcp .
docker run -p 7860:7860 tabicl-mcp
# MCP endpoint: http://localhost:7860/mcp   ·   healthcheck: /health

Why TabICL (vs TabPFN)

Development

python -m venv .venv && .venv/bin/pip install -e ".[dev]"
.venv/bin/pytest -m "not slow"   # fast tests
.venv/bin/pytest                 # includes real-model tests (downloads checkpoints)

Layout: tabicl_mcp/server.py (MCP tools) · data.py (ingestion + cache) · ml.py (evaluate/predict/importance) · report.py (HTML reports).

MIT licensed. TabICL itself is by soda-inria (BSD-3). PRs welcome.