mcprojsim 0.4.6

Monte Carlo simulation system for software development effort estimation

Monte Carlo Project Simulator (mcprojsim)

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Overview

mcprojsim is a Monte Carlo simulation tool for project with emphasis on agile software project estimation. Instead of producing a single deadline, it models uncertainty in task duration, dependencies, risks, and other schedule drivers to produce confidence-based forecast ranges.

It is intended for teams that want answers such as:

• What is the likely completion range for this project?
• What is the $P50$, $P80$, or $P90$ delivery date?
• Which tasks most often drive schedule risk?
• How do risks and uncertainty factors change the forecast?

Key features

• Natural language project input — generate valid project files from plain-text descriptions using mcprojsim generate
• Monte Carlo schedule simulation with configurable iteration counts
• Range-based task estimates using triangular and log-normal distributions
• Unit-aware estimation: supports hours, days, and weeks with automatic conversion to a canonical hours-based internal representation
• Configurable hours_per_day per project, with working-day and delivery-date reporting
• Task dependencies and schedule-aware project duration calculation
• Task-level and project-level risk modeling
• Configurable uncertainty factors such as team experience and requirements maturity
• T-shirt size and story point symbolic estimates with configurable unit defaults
• Exported results in JSON, CSV, and HTML formats
• Critical path and sensitivity-oriented analysis outputs
• Sensitivity analysis — Spearman rank correlation identifies which tasks most influence total duration
• Schedule slack — CPM-based total float calculation highlights critical vs. buffered tasks
• Risk impact analysis — per-task trigger rates, mean impact, and mean-when-triggered statistics
• Statistical distribution metrics — skewness, excess kurtosis, and coefficient of variation for the overall schedule distribution
• Probability-of-date — calculate the likelihood of finishing by a given target date (--target-date)
• ASCII table output — optional --table flag formats CLI results as bordered tables for easier reading
• Reproducible runs with explicit random seeds

Recommended installation

For most end users, pipx is the simplest way to install mcprojsim as a CLI tool.

python3 -m pip install --user pipx
python3 -m pipx ensurepath
pipx install mcprojsim

Then verify the installation:

mcprojsim --help
mcprojsim --version

For a first-run walkthrough, see the 10-min QUICKSTART.md. After this we recommend going through the User Guide

Minimal example

Create a file named project.yaml:

project:
  name: "My Project"
  description: "Sample project for estimation"
  start_date: "2025-11-01"
  confidence_levels: [50, 80, 90]

tasks:
  - id: "task_001"
    name: "Database schema design"
    estimate:
      min: 3
      most_likely: 5
      max: 10
      unit: "days"
    dependencies: []
    uncertainty_factors:
      team_experience: "high"
      requirements_maturity: "medium"
      technical_complexity: "low"

Validate the file:

mcprojsim validate project.yaml

Run a simulation:

mcprojsim simulate project.yaml --seed 12345

Typical outputs (see the --help for how to specify output) include:

• *_results.json for full machine-readable output
• *_results.csv for tabular summaries
• *_results.html for a browsable report

Documentation map

Use the local document that matches your goal:

• QUICKSTART.md — installation paths, first commands, container usage, and local setup
• docs/getting_started.md — first simulation walkthrough
• docs/user_guide/introduction.md — concepts behind Monte Carlo estimation
• docs/user_guide/your_first_project.md — build a project file step by step
• docs/user_guide/project_files.md — project file reference
• docs/configuration.md — uncertainty factors and runtime configuration
• docs/examples.md — example projects and usage patterns
• docs/api_reference.md — Python API usage

The full published documentation is also available at https://johan162.github.io/mcprojsim/.

Example commands

# Generate a project file from a natural language description
mcprojsim generate examples/nl_example.txt -o my_project.yaml

# Validate an input file
mcprojsim validate examples/sample_project.yaml

# Run a default simulation
mcprojsim simulate examples/sample_project.yaml

# Use a custom configuration
mcprojsim simulate examples/sample_project.yaml --config examples/sample_config.yaml

# Reproduce a run exactly
mcprojsim simulate examples/sample_project.yaml --seed 42

# Format output as ASCII tables
mcprojsim simulate examples/sample_project.yaml --table

# Calculate probability of meeting a target date
mcprojsim simulate examples/sample_project.yaml --target-date 2026-06-01

# Combine options
mcprojsim simulate examples/sample_project.yaml --config examples/sample_config.yaml --table --verbose --seed 42

MCP server integration

mcprojsim can run as a Model Context Protocol (MCP) server, letting AI assistants such as GitHub Copilot, Claude Desktop, or any MCP-compatible client generate project files, validate descriptions, and run simulations conversationally.

Install with the optional MCP dependency group:

pipx install "mcprojsim[mcp]"

Or, from a source checkout:

poetry install --with mcp

Add the server to your MCP client configuration (e.g. VS Code settings.json or Claude Desktop claude_desktop_config.json):

{
  "mcpServers": {
    "mcprojsim": {
      "command": "mcprojsim-mcp"
    }
  }
}

The server exposes three tools:

Tool	Description
generate_project_file	Convert a natural-language project description into a valid YAML project file
validate_project_description	Check a description for missing data or inconsistencies without generating a file
simulate_project	Generate, validate, and simulate in one step — returns full statistical results

For developers

If you want to work from a source checkout, run tests, build docs, or use containers, start with:

• QUICKSTART.md
• scripts/README.md
• docs/index.md

The deatiled developer documentation (including how to configure and build the container) is available at

• docs/development.md

Contributing

Contributions are welcome.

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

Support

• GitHub Issues: https://github.com/johan162/mcprojsim/issues
• Documentation site: https://johan162.github.io/mcprojsim/

Citation

If you use this tool in research or project planning, please cite:

@software{mcprojsim,
  title = {Monte Carlo Project Simulator},
  author = {Johan Persson},
  year = {2026},
  url = {https://github.com/johan162/mcprojsim},
  version = {0.4.6}
}

License

MIT License - see LICENSE.

Acknowledgments

Inspired by the work of:

• Steve McConnell - Software Estimation: Demystifying the Black Art
• Frederick Brooks - The Mythical Man-Month
• Douglas Hubbard - How to Measure Anything in Cybersecurity Risk