raspa-mcp

Turn any AI agent into a molecular simulation expert — overnight.

raspa-mcp is a Model Context Protocol (MCP) server that wraps RASPA2 — the gold-standard molecular simulation engine for porous materials — into a clean, agent-friendly tool layer. Feed it a CIF file and a molecule name; let your agent handle the rest.

Why raspa-mcp?

Running RASPA2 correctly requires deep expertise: choosing the right ensemble, setting unit cell replications, picking force fields, validating Ewald summation parameters, and parsing Fortran-style output files. Historically this knowledge lived in the heads of computational chemists and nowhere else.

raspa-mcp encodes that expertise as 24 structured MCP tools — covering every major simulation type RASPA2 supports — so that an LLM agent like featherflow can autonomously design, validate, execute, and interpret molecular simulations without human intervention.

Features at a Glance

Simulation Templates (12 types)

| Template | Purpose | |----------|---------| | GCMC | Grand Canonical Monte Carlo — adsorption isotherms | | Widom | Widom test-particle insertion — Henry coefficient at infinite dilution | | VoidFraction | Helium void fraction (prerequisite for GCMC) | | NVT-MC | Fixed-N Monte Carlo — configurational sampling, RDF | | NPT-MC | Variable-volume MC — equilibrium density, flexible cell | | MD | NVT Molecular Dynamics — diffusion, transport | | NPT-MD | Constant-pressure MD — thermal expansion | | NVE-MD | Microcanonical MD — energy conservation benchmarking | | GCMCMixture | Binary mixture GCMC — co-adsorption, selectivity | | CBMC | Configurational-Bias MC — chain/flexible molecules (C4+) | | TI | Thermodynamic Integration — free energy ΔA | | FlexibleMD | Flexible-framework MD — breathing, gate opening |

Output Parsing (7 parsers)

Adsorption loading — mol/kg, mg/g, cm³(STP)/g, molecules/uc, ±errors
Isosteric heat Qst — from energy fluctuations [kJ/mol]
Henry coefficient & μ_ex — from Widom insertion, −RT ln(W)
Helium void fraction — direct extraction
Radial distribution function g(r) — peak detection, full r/g(r) arrays
MSD → Diffusion coefficients — self D_s and collective D_c via Einstein relation (NumPy linear fit, latter 50% of trajectory)
3D density grid — 2D slice extraction from .grid files
Thermodynamic Integration — trapezoidal ∫⟨∂U/∂λ⟩dλ → ΔA [kJ/mol]
Multi-component mixture — per-component loading with backward compatibility

Analysis Tools

Selectivity S_AB — (x_A/x_B) / (y_A/y_B) from mixture loadings
Isotherm plotting — single and multi-MOF comparison PNGs (matplotlib)
Density slice plotting — heatmap PNG from 3D grid data

Built-in Knowledge Base

5 molecules: CO2, N2, CH4, H2O, helium, n-butane (TraPPE / SPC-E)
5 force fields: TraPPE-CO2/N2/CH4/H2O, UFF — with mixing rules, pseudo-atom definitions
Input validator — catches 20+ common mistakes before RASPA2 ever runs
Environment checker & installer — conda and source install, 6-shell support

Installation

# 1. Clone
git clone https://github.com/your-org/raspa-mcp
cd raspa-mcp

# 2. Create virtualenv (Python 3.11+)
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate

# 3. Install
pip install -e ".[dev]"

RASPA2 itself is not bundled. Install it separately:

# via conda (recommended)
conda install -c conda-forge raspa2

# or let raspa-mcp install it for you
raspa-mcp  # then call install_raspa2() from your agent

RASPA2 source: https://github.com/iRASPA/RASPA2 Reference: D. Dubbeldam, S. Calero, D.E. Ellis, R.Q. Snurr, Mol. Simul. 42, 81–101 (2016)

Quickstart — featherflow

Add to your featherflow config:

{
  "tools": {
    "mcpServers": {
      "raspa2": {
        "command": "python",
        "args": ["-m", "raspa_mcp.server"],
        "toolTimeout": 30,
        "env": {}
      }
    }
  }
}

Your agent can now autonomously:

User: Study CO2 adsorption in ZIF-8 at 298 K from 0.1 to 50 bar.

Agent:
  1. raspa-mcp.check_raspa2_environment()
  2. raspa-mcp.get_simulation_template("VoidFraction")   → run RASPA2
  3. raspa-mcp.parse_raspa_output(...)                   → void fraction = 0.47
  4. raspa-mcp.get_simulation_template("GCMC")           → fill placeholders × 7 pressures
  5. raspa-mcp.validate_simulation_input(...)            → clean
  6. shell_exec → RASPA2 × 7
  7. raspa-mcp.parse_raspa_output(...)                   → isotherm data
  8. raspa-mcp.plot_isotherm(...)                        → ZIF-8_CO2.png
  9. feishu-mcp.upload_file_and_share(...)               → report delivered

No human intervention required.

MCP Tools Reference

| Tool | Category | |------|----------| | list_simulation_types | Discovery | | get_simulation_template | Input generation | | get_parameter_docs | Input generation | | list_available_forcefields | Force field | | get_forcefield_files | Force field | | recommend_forcefield | Force field | | list_available_molecules | Molecule | | get_molecule_definition | Molecule | | create_workspace | Workspace | | validate_simulation_input | Validation | | parse_raspa_output | Output parsing | | parse_rdf_output | Output parsing | | parse_msd_output | Output parsing | | parse_ti_output | Output parsing | | parse_density_grid | Output parsing | | calculate_selectivity | Analysis | | plot_isotherm | Visualization | | plot_isotherm_comparison | Visualization | | plot_density_slice | Visualization | | check_raspa2_environment | Environment | | install_raspa2 | Environment |

Testing

pytest tests/ -q          # 41 tests, ~1.5 s
ruff check raspa_mcp/ tests/

Architecture

raspa-mcp/
├── raspa_mcp/
│   ├── server.py        # 21 MCP tools (FastMCP, stdio transport)
│   ├── parser.py        # Output parsers (loading, RDF, MSD, TI, density)
│   ├── validator.py     # Input validator (20+ rule checks)
│   ├── installer.py     # RASPA2 env detection + conda/source install
│   └── data/
│       ├── templates.py    # 12 simulation.input templates
│       ├── molecules.py    # 6 molecule definitions + metadata
│       └── forcefields.py  # 5 force field file sets
├── tests/
│   └── test_server.py   # 41 unit tests
└── docs/
    └── workflow.md      # Full autonomous research workflow walkthrough

Full Business Workflow

See docs/workflow.md for a complete end-to-end walkthrough of an autonomous MOF screening study using featherflow + raspa-mcp + RASPA2 + feishu-mcp, from a single chat message to a ranked report delivered to Feishu — approximately 120–140 tool calls, zero human steps.

License

MIT

Acknowledgements

Built on top of RASPA2 by Dubbeldam, Calero, Ellis & Snurr. Force-field parameters from the TraPPE family (Martin, Siepmann et al.) and the Universal Force Field (Rappé et al.).

MCP Servers