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simulation_by_simpy_mcp

Simulate M/M/1, M/M/c, and manufacturing (MPS) systems to forecast wait times, utilization, and makespan. Compare separate versus pooled queues and get parameter recommendations to meet service targets. Analyze results with theory-backed metrics, schedule insights, and clear stability checks.

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About simulation_by_simpy_mcp

simulation_by_simpy_mcp is an MCP server in the Files category: simulate M/M/1, M/M/c, and manufacturing (MPS) systems to forecast wait times, utilization, and makespan. Compare separate versus pooled queues and get parameter recommendations to meet service targets. Analyze results with theory-backed metrics, schedule insights, and clear stability checks. It has been installed 0 times through Conduid.

Install

Clone
git clone https://github.com/kiyoung8/simulation_by_simpy_mcp

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README

MCP Server for M/M/1 and M/M/c Queue Simulation

A Model Context Protocol server that provides comprehensive resources, tools, and prompts for M/M/1 and M/M/c queuing system simulation and analysis.

New in v0.2.0: Full M/M/c support with comparison tools!

Python MCP

What is M/M/1 and M/M/c?

M/M/1 and M/M/c are fundamental queuing models in operations research:

  • First M: Markovian (Poisson) arrivals
  • Second M: Markovian (exponential) service times
  • 1 or c: Number of servers (1 for M/M/1, c for M/M/c)

This MCP server enables LLMs like Claude to:

  • Simulate both M/M/1 and M/M/c systems
  • Compare separate queues vs pooled queue strategies
  • Validate simulation parameters
  • Calculate theoretical performance metrics
  • Generate and execute SimPy simulations
  • Analyze real-world scenarios (supermarkets, banks, etc.)

Features

📚 Resources (7)

  • mm1://schema - Complete M/M/1 system schema
  • mm1://parameters - Parameter definitions with constraints
  • mm1://metrics - Performance metrics catalog
  • mm1://formulas - Theoretical formulas
  • mm1://guidelines - Implementation best practices
  • mm1://examples - Pre-configured scenarios
  • mm1://literature - References and citations

🔧 Tools (8)

  • validate_config - Validate M/M/1 parameters and check stability
  • calculate_metrics - Compute theoretical performance metrics
  • run_simulation - Execute M/M/1 simulation (legacy)
  • run_mmc_simulation - Execute M/M/c simulation (NEW!)
  • compare_mm1_vs_mmc - Compare separate vs pooled queues (NEW!)
  • analyze_cashier_problem - Supermarket scenario analysis (NEW!)
  • compare_results - Analyze simulation accuracy
  • recommend_parameters - Suggest optimal configuration

💬 Prompts (4)

  • generate_simulation_code - Create production-ready SimPy code
  • explain_mm1_theory - Educational content on M/M/1 theory
  • analyze_results - Interpret simulation outcomes
  • debug_simulation - Troubleshoot common issues

Installation

Option 1: Using uvx (Recommended)

uvx mcp-server-mm1

Option 2: Using pip

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

Option 3: From Source

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

Usage with Claude Desktop

Add to your claude_desktop_config.json:

macOS

Location: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows

Location: %APPDATA%\Claude\claude_desktop_config.json

Configuration

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

Restart Claude Desktop and the server will be available!

Example Usage in Claude

1. Get M/M/1 Schema

User: Show me the M/M/1 queue schema

Claude uses: mm1://schema resource

2. Validate Configuration

User: Is λ=5, μ=8 a valid M/M/1 configuration?

Claude uses: validate_config tool
Result: ✓ Valid, ρ=0.625

3. Calculate Theoretical Metrics

User: Calculate theoretical metrics for λ=5, μ=8

Claude uses: calculate_metrics tool
Result:
- Utilization: 0.625
- Avg queue length: 1.0417
- Avg waiting time: 0.2083
- Avg system time: 0.3333

4. Run Simulation

User: Run a simulation with λ=5, μ=8 for 10,000 time units

Claude uses: run_simulation tool
Result: Simulation metrics + theoretical comparison + accuracy analysis

5. Generate Code

User: Generate SimPy code for λ=3, μ=10

Claude uses: generate_simulation_code prompt
Result: Complete, production-ready Python code

Development

Setup Development Environment

# Clone repository
git clone https://github.com/yourusername/mcp-server-mm1.git
cd mcp-server-mm1

# Install dependencies
uv pip install -e ".[dev]"

Run Tests

pytest

Code Quality

# Format code
black src/ tests/

# Lint
ruff check src/ tests/

# Type check
mypy src/

Local Testing

Use the MCP Inspector to test the server locally:

# Install MCP inspector (if not already installed)
npm install -g @modelcontextprotocol/inspector

# Run server with inspector
mcp dev src/mcp_server_mm1/server.py

This opens a web interface where you can:

  • Browse available resources
  • Test tool invocations
  • Try prompt templates
  • Inspect JSON-RPC communication

Architecture

src/mcp_server_mm1/
├── server.py          # FastMCP server with resources/tools/prompts
├── schemas/
│   └── mm1_schema.py  # M/M/1 system schema definition
├── simulations/
│   └── mm1_queue.py   # SimPy simulation implementation
└── utils/
    └── metrics.py     # Theoretical calculations

M/M/1 Theory Quick Reference

Key Formulas

Given arrival rate λ and service rate μ:

  • Utilization: ρ = λ/μ
  • Avg Queue Length: L_q = ρ²/(1-ρ)
  • Avg Time in Queue: W_q = ρ/(μ(1-ρ))
  • Avg Time in System: W = 1/(μ(1-ρ))

Stability Condition

System must satisfy ρ < 1 (λ < μ)

If ρ ≥ 1, the queue grows unbounded!

Contributing

Contributions are welcome! Please:

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes with tests
  4. Submit a pull request

License

MIT License - see LICENSE file for details.

Related Work

This MCP server was developed as part of research on LLM-assisted simulation code generation for the Winter Simulation Conference (WSC) 2025.

References

Support


Made with ❤️ for the simulation and LLM communities

README mirrored from the source repository 4 months ago. The original is authoritative.

Questions

About simulation_by_simpy_mcp

How do I install simulation_by_simpy_mcp?

Run git clone https://github.com/kiyoung8/simulation_by_simpy_mcp, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is simulation_by_simpy_mcp safe to use with an AI agent?

Its trust score is 34 out of 100 (low). Conduid hasn't run static security checks on this repository yet, so review the source yourself before granting it credentials. It has no ConduID identity yet, so agent calls to it are not receipted.

Is simulation_by_simpy_mcp still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.