root-mcp

ROOT-MCP empowers Large Language Models (LLMs) to natively understand and analyze CERN ROOT files.

By exposing a set of specialized tools via the Model Context Protocol (MCP), it turns Claude (and other MCP-compliant agents) into capable physics research assistants that can:

• Inspect ROOT file structures (Trees, Branches, Histograms)
• Analyze data distributions (Compute Histograms, Statistics)
• Compute kinematic quantities (Invariant Mass)
• Visualize results (Plot 1D/2D histograms directly)
• Filter data using physics cuts ("selections")

Why this matters: Instead of asking an LLM to "write a script" that you have to debug and run, you can ask the LLM to "Check the muon pT distribution in this file" and it will just do it.

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Architecture

ROOT-MCP features a dual-mode architecture:

• Core Mode: File I/O, data reading, and basic statistics
• Extended Mode: Full analysis capabilities including fitting, kinematics, and correlations

The mode is controlled via configuration, and the server automatically loads only the components you need. Runtime mode switching is also available.

Optional Native ROOT Support

ROOT-MCP can optionally integrate with a native ROOT/PyROOT installation to unlock capabilities beyond what uproot provides:

• run_root_code: Execute arbitrary PyROOT/Python code and get structured results
• run_rdataframe: Compute histograms using ROOT's RDataFrame (no boilerplate needed)
• run_root_macro: Execute C++ ROOT macros via gROOT.ProcessLine

This feature is entirely optional — ROOT-MCP works fully without ROOT installed. When ROOT is available and enabled, these additional tools appear automatically.

Requirements: A working ROOT installation (via conda-forge, system package, or binary tarball). ROOT is not pip-installable at this time.

Enable it by setting enable_root: true in your config.yaml:

features:
  enable_root: true

# Optional: tune execution settings
root_native:
  execution_timeout: 60
  working_directory: "/tmp/root_mcp_native"

Use get_server_info to check ROOT availability at runtime:

{
  "root_native_available": true,
  "root_native_enabled": true,
  "root_version": "6.32/02",
  "root_features": {"rdataframe": true, "roofit": true, "tmva": false}
}

Quick Start

1. Install

pip install root-mcp

Optional: For remote file access via XRootD protocol:

pip install "root-mcp[xrootd]"

2. Configure

Fastest path — no config file needed:

root-mcp --data-path /path/to/your/data

Or set an environment variable once:

export ROOT_MCP_DATA_PATH=/path/to/your/data

Zero-config one-liners:

# Core mode (lightweight, no scipy/matplotlib needed)
root-mcp --data-path /data --mode core

# Extended mode with native ROOT, restricted to one directory
root-mcp --data-path /data --enable-root --allowed-root /data

# Remote XRootD resource, no YAML needed
root-mcp --resource cms=root://xrootd.cern.ch//store --allow-remote --mode extended

# Docker / container — fully env-var driven
ROOT_MCP_DATA_PATH=/data ROOT_MCP_MODE=extended ROOT_MCP_EXPORT_PATH=/exports root-mcp

# Quiet server (only warnings+) with a cache increase
root-mcp --data-path /data --log-level WARNING --cache-size 100

Generate a starter config (optional):

root-mcp init --permissive   # creates config.yaml pre-filled with current directory

Manual config file — for persistent settings, remote resources, or native ROOT:

server:
  mode: "extended"   # "core" or "extended"

resources:
  - name: "my_analysis"
    uri: "file:///path/to/data"
    allowed_patterns: ["*.root"]

security:
  allowed_roots: []  # empty = any local path is accessible (permissive)

Mode Selection:

• mode: "core" — Lightweight: file operations and basic statistics
• mode: "extended" — Full analysis: histograms, fitting, kinematics, correlations

Switch modes at runtime with the switch_mode tool — no restart required.

3. Run with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "root-mcp": {
      "command": "root-mcp",
      "args": ["--data-path", "/path/to/your/data"]
    }
  }
}

Or with a persistent config file:

{
  "mcpServers": {
    "root-mcp": {
      "command": "root-mcp",
      "env": {
        "ROOT_MCP_CONFIG": "/path/to/config.yaml"
      }
    }
  }
}

Documentation

The full documentation site is built with Sphinx and covers installation, configuration, all 20 MCP tools, LLM integration patterns, and the developer guide with auto-generated API reference.

Read online: The docs are hosted at root-mcp docs

pip install "root-mcp[docs]"
./scripts/build_docs.sh
# open docs/_build/html/index.html

For live-reload while writing docs:

cd docs && make livehtml

Highlights:

• User Guide — installation, quickstart, modes, configuration, LLM integration
• Tool Reference — complete catalogue of all tools and their JSON payloads
• Developer Guide — architecture, module overview, dev setup, contributing
• API Reference — auto-generated from source docstrings

Citation

If you use ROOT-MCP in your research, please cite:

@software{root_mcp,
  title = {ROOT-MCP: Production-Grade MCP Server for CERN ROOT Files},
  author = {Mohamed Elashri},
  year = {2025},
  url = {https://github.com/MohamedElashri/root-mcp}
}

License

MIT License - see LICENSE for details.