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An MCP server that embedsPyMOL in-process, headless — no GUI, no socket plugin, no manual setup. It exposes 30+ typed tools, returns rendered images inline so the model can see what it draws, loads GROMACS/LAMMPS trajectories, and ships a clathrate-hydrate analysis toolkit (H-bond networks, F3/F4 order parameters) with numerically validated science.

Demo

Related MCP server: vmd-mcp

Highlights

Runtime

Embedded, headless pymol2 — no GUI, no plugin, no socket

Tools

30+ typed tools with real, structured return values

Vision

ray-traced PNG returned inline so the model sees what it draws

MD trajectories

GROMACS .xtc/.trr + LAMMPS dump (MDAnalysis bridge)

Domain science

cage perception (TRACE), occupancy, H-bonds, F3/F4 — validated

Robustness

worker-thread session, stdout-safe transport, pytest suite

Safety

arbitrary-code passthrough off by default

Features

  • Embedded & headless — one long-lived PyMOL instance on a dedicated worker thread; nothing to click, works in CI.

  • The model can seerender_image ray-traces and returns a PNG as MCP image content.

  • MD-native — load GROMACS .gro+.xtc, or bridge LAMMPS/NetCDF/… through MDAnalysis with in-memory coordinate injection.

  • Clathrate-hydrate toolkit — H-bond networks and F3/F4 order parameters ported from a validated Rust engine, all in nm with correct triclinic PBC.

  • Typed, safe tools — every argument is schema-validated; the arbitrary-code passthrough is opt-in (PYMOL_MCP_ALLOW_CODE_EXEC=1).

  • Protocol-hardened — PyMOL's chatty stdout is permanently redirected so it can never corrupt the JSON-RPC stream (with a subprocess test that proves it).

Quick Start

IMPORTANT

PyMOL open-source is aconda package, and the server must run in a Python that can import pymol2. Install into that interpreter — do not use uvx/fastmcp install (they build isolated envs without PyMOL).

# 1. Create the environment (or reuse one that already has pymol-open-source)
conda env create -f env.yml        # env named `pymol-mcp`
conda activate pymol-mcp

# 2. Install this package (with the optional MD bridge + dev tools)
pip install -e ".[md,dev]"

# 3. Verify
pytest -q

It's a standard MCP server over stdio, so it works with any MCP-capable client (Claude Code / Desktop, Codex CLI, Gemini CLI, Cline, Continue, …). Point the command at the absolute conda interpreter so it can import pymol2.

Most clients use an mcpServers block (Claude Code / Desktop, Gemini CLI, Cline, Continue, …):

{
  "mcpServers": {
    "pymol": {
      "command": "/absolute/path/to/conda/envs/pymol-mcp/bin/python",
      "args": ["-m", "pymol_mcp"]
    }
  }
}
[mcp_servers.pymol]
command = "/absolute/path/to/conda/envs/pymol-mcp/bin/python"
args = ["-m", "pymol_mcp"]

Prefer not to hardcode a path? Use "command": "conda", "args": ["run", "-n", "pymol-mcp", "python", "-m", "pymol_mcp"] instead (requires conda on the client's PATH). See llms-install.md for a full from-scratch setup.

To enable the opt-in scripting tools, add "env": {"PYMOL_MCP_ALLOW_CODE_EXEC": "1"} to the server entry.

Then ask your agent things like:

Load ./hydrate.gro, color water by F4 order parameter, and render it.
Load md.gro + traj.xtc, show CO2 guests as spheres, render frame 50.
What's the mean H-bond coordination of the water in this structure?

Tool Catalog

Group

Tools

Session / IO

load_structure · fetch_pdb · list_objects · get_object_info · reset_session

Selection

select · get_selection_info

Representation

show · hide · color · spectrum · set_background

View / Render

orient · zoom · turn · render_image → 🖼️ inline PNG

Measurement

measure_distance · measure_angle · measure_dihedral · align · save_file

Trajectory / MD

load_trajectory (GROMACS/DCD) · load_trajectory_mda (LAMMPS/NetCDF via MDAnalysis)

Clathrate domain

identify_cages (TRACE) · cage_occupancy · mark_cages · hbond_network · order_parameter (F3 / F4)

Scripting (opt-in)

run_pml · run_python

Domain: clathrate-hydrate science

Ported from a validated Rust reference implementation and re-checked against ground truth. All analysis runs in nanometres with a correct fractional-coordinate minimum-image convention (orthorhombic and triclinic), a signed atan2 dihedral for F4, and a periodic-image KDTree for neighbour search.

  • identify_cages — full TRACE cage perception: ring finding → geometric validation → constraint-propagation assembly → Euler (SEC) validation → face-count typing (5¹², 5¹²6², 5¹²6⁴, …) and an sI/sII/sH structure call.

  • cage_occupancy — assign guest molecules (CO₂/CH₄) to cages and report per-type occupancy (θ_S, θ_L).

  • mark_cages — drop a colored sphere at each cage centre so render_image can show the cage lattice.

  • order_parameter — F4 (torsional) and F3 (three-body angular). F4 ≈ 0.7–0.95 → hydrate, ≈ 0 → liquid, ≈ −0.4 → ice Ih.

  • hbond_network — water H-bond graph (O–O ≤ 0.36 nm and a donor H–O···O angle < 35°) with coordination stats.

TIP

Validated against ground truth: on a structure II reference, identify_cages finds exactly **128 × 5¹²

  • 64 × 5¹²6⁴** cages (the textbook 2:1 sII lattice), and on structure I exactly 16 × 5¹² + 48 × 5¹²6²; F4 over the first ten waters reproduces the reference value 0.926698 exactly, F3 = 0.0028 (hydrate-like ≤ 0.04), and the H-bond network is a perfect tetrahedral (mean coordination 4.00) framework.

How it works

   MCP client (Claude · Codex · Gemini …)
          │  stdio JSON-RPC
          ▼
 ┌───────────────────────────────────────────────┐
 │  pymol-mcp  (FastMCP, conda env with pymol2)    │
 │   • permanent stdout redirect (protocol-safe)   │
 │   • ONE worker thread owns + drives pymol2      │
 │   • typed @mcp.tool functions                   │
 └───────────────────────────────────────────────┘
      │ cmd.* (headless)        │ numpy / scipy (nm)
      ▼                          ▼
  PyMOL 3.x  ── ray → PNG    analysis/ (hbond, F3/F4)
                              coords via iterate_state

Requirements

Dependency

Required

Purpose

Python 3.11+ (conda)

Yes

Runtime that can import pymol2

pymol-open-source 3.x

Yes

The visualization engine (conda)

fastmcp 3.x, numpy, scipy

Yes

MCP server + analysis

MDAnalysis

No (extra md)

LAMMPS / NetCDF / xtc bridge (GPL-2.0+)

ffmpeg

No

Movie export (future)

Contributing

Issues and PRs welcome — see CONTRIBUTING.md.

License

MIT. The optional md extra pulls MDAnalysis (GPL-2.0-or-later), imported lazily; the core package stays MIT.

A
license - permissive license
-
quality - not tested
B
maintenance

Maintenance

Maintainers
Response time
Release cycle
1Releases (12mo)
Commit activity

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