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MMQ MCP Server

Provides M/M/1 and M/M/c queue simulation tools, metrics, and production-ready SimPy code.

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "kiyoung8-simulation_by_simpy_mcp": {
      "command": "uvx",
      "args": [
        "mcp-server-mm1"
      ]
    }
  }
}

You can run, test, and analyze M/M/1 and M/M/c queue simulations through an MCP server that provides prebuilt resources, prompts, and execution tools. This server helps you validate configurations, generate production-ready simulations, compare queue strategies, and analyze real-world scenarios like stores or banks using SimPy.

How to use

To use the MCP server, connect your MCP client and select the M/M/1 or M/M/c workspace. You can validate configurations, compute theoretical metrics, run simulations, and generate SimPy code for production use. Start by choosing the desired operation (validate, simulate, compare, or analyze), then provide your λ (arrival rate) and μ (service rate) values along with the number of servers for M/M/c. Use the results to compare separate queues versus a pooled queue, and to interpret simulated performance against theoretical formulas.

How to install

Follow these concrete installation flows to set up the M/M/1/M/M/c MCP server and start running simulations.

Install using uvx (Recommended)

uvx mcp-server-mm1

Install using pip

pip install mcp-server-mm1
mcp-server-mm1

Install from source

git clone https://github.com/yourusername/mcp-server-mm1.git
cd mcp-server-mm1
uv pip install -e .
mcp-server-mm1

Configure Claude Desktop (example setup)

If you use Claude Desktop, you can register the MCP server by adding a configuration entry that points to the runtime command. The following example shows a typical setup referencing the uvx runner.

{
  "mcpServers": {
    "mm1-simulation": {
      "command": "uvx",
      "args": ["mcp-server-mm1"]
    }
  }
}

Available tools

validate_config

Validate M/M/1 parameters and check system stability before running simulations.

calculate_metrics

Compute theoretical performance metrics such as utilization, average queue length, and waiting times.

run_simulation

Execute an M/M/1 style simulation to generate metrics and compare with theory.

run_mmc_simulation

Execute an M/M/c simulation to handle multiple servers and compare against separate queues.

compare_mm1_vs_mmc

Compare separate queues versus a pooled queue to evaluate strategy performance.

analyze_cashier_problem

Analyze a supermarket-like scenario to study queue dynamics and staffing.

compare_results

Analyze the alignment between simulation outcomes and theoretical expectations.

recommend_parameters

Suggest optimal λ and μ configurations to meet stability and performance goals.

generate_simulation_code

Create production-ready SimPy code from given parameters.

explain_mm1_theory

Provide educational content on M/M/1 theory and core formulas.

analyze_results

Interpret simulation outcomes and extract actionable insights.

debug_simulation

Troubleshoot common issues in configurations and simulations.