Kappybara MCP Server

An MCP (Model Context Protocol) server for running Kappa simulations using the Kappybara package. This allows LLMs to run rule-based simulations of molecular interaction systems directly.

Python packages used:

• Kappybara

• FastMCP -- deployed on https://kappybara.fastmcp.app/mcp

Features

• Run Kappa Simulations: Execute Kappa models with customizable parameters using kappybara

• CSV Output: Get simulation results as CSV data for easy analysis

• Example Models: Built-in example models (reversible binding, linear polymerization)

• Pure Python: Uses the kappybara Python library for all simulations

Related MCP server: SMILES Visualizer MCP Server

Installation

1. Clone or download this repository

2. Install dependencies:

pip install -r requirements.txt

Usage

Running the MCP Server

fastmcp run main.py

Using with Claude Desktop

fastmcp install claude-desktop main.py

Available Tools

run_kappa_simulation

Run a Kappa simulation and return results with stdout, stderr, and CSV output.

Parameters:

• kappa_code (string, required): The Kappa model code to simulate

• time_limit (float, default: 100.0): Maximum simulation time

• points (int, default: 200): Number of data points to collect

• seed (int, optional): Random seed for reproducibility

Returns: JSON string with three fields:

• stdout: Standard output from the simulation

• stderr: Standard error output (warnings, errors)

• output: CSV data with simulation results

Example:

kappa_code = """
%init: 100 A(x[.])
%init: 100 B(x[.])

%obs: 'AB' |A(x[1]), B(x[1])|

A(x[.]), B(x[.]) <-> A(x[1]), B(x[1]) @ 1, 1
"""

result = run_kappa_simulation(kappa_code, time_limit=50, points=100)
# Result is a JSON string like:
# {
#   "stdout": "",
#   "stderr": "",
#   "output": "time,AB\n0.0,0\n0.01,1\n..."
# }

Available Resources

kappa://examples/simple

A simple reversible binding model (kappybara syntax).

kappa://examples/polymerization

A linear polymerization model (kappybara syntax).

Kappa Language Basics (Kappybara Syntax)

Kappybara uses a specific syntax for Kappa models. Here's a quick reference:

Basic Syntax

%init: 100 AgentName(site1[.], site2[state])  # Initialize agents
%obs: 'Observable' |pattern|                   # Define observable
pattern -> pattern @ rate                      # Define rule (irreversible)
pattern <-> pattern @ rate1, rate2             # Define rule (reversible)

Binding Sites

• [.] - unbound site

• [1], [2], etc. - bound sites (bond labels)

• [_] - wildcard for any binding state

Example: Reversible Binding

%init: 100 A(x[.])
%init: 100 B(x[.])

%obs: 'A_free' |A(x[.])|
%obs: 'B_free' |B(x[.])|
%obs: 'AB_complex' |A(x[1]), B(x[1])|

// Reversible binding with forward rate 0.001 and reverse rate 0.1
A(x[.]), B(x[.]) <-> A(x[1]), B(x[1]) @ 0.001, 0.1

Development

Project Structure

kappybara-mcp/
├── main.py              # MCP server implementation
├── requirements.txt     # Python dependencies
├── README.md           # This file
└── test_example.py     # Example test/demo script

Testing

Run the test example:

python test_example.py

How It Works

1. The MCP server exposes Kappa simulation capabilities through the Model Context Protocol

2. LLMs can call the run_kappa_simulation tool with Kappa code (using kappybara syntax) and parameters

3. The server uses the Kappybara Python library to parse the model and run the simulation

4. Results are returned as JSON with stdout, stderr, and CSV output that can be analyzed or visualized

References

• Kappybara Documentation

• FastMCP Documentation

• Kappa Language

• Original ChatGPT Action

License

MIT