GreenCodeMCP

An MCP-based tool for sustainable software maintenance and resource-aware refactoring.

Accepted at IEEE ICSME 2026 — Tool Demonstration and Data Showcase Track

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Overview

GreenCodeMCP is a developer-facing tool that connects modern AI coding assistants to a sustainable refactoring workflow via the Model Context Protocol (MCP). Given Python code, it:

1. Detects energy anti-patterns using 20 AST-based rules
2. Retrieves optimization evidence from an 800-example knowledge base
3. Suggests refactored code (deterministic auto-fix + LLM-assisted)
4. Validates behavioral preservation (syntax, signature, tests, output hash)
5. Benchmarks resource gains under a controlled protocol (time, memory, energy, CO2)
6. Reports results as structured JSON or Markdown

It integrates directly into VS Code, Cursor, and Windsurf via MCP.

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Installation

Prerequisites

• Python >= 3.10
• No GPU required
• (Optional) NVIDIA API key for LLM-assisted refactoring — get one free

Setup

# Clone the repository
git clone https://github.com/DaoudiAmir/GreenCodeMCP.git
cd GreenCodeMCP

# Create and activate a virtual environment
python -m venv .venv

# Windows
.venv\Scripts\activate
# Linux / macOS
source .venv/bin/activate

# Install the package
pip install -e .

# (Optional) Configure LLM access
cp .env.example .env
# Edit .env and set NVIDIA_API_KEY=your_key_here

Note: Without an API key, all features work except LLM-assisted generation. The deterministic auto-fix (7 rules), KB retrieval, validation, and benchmarking are fully offline.

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Running the MCP Server

Start the server with:

python -m src.greencode_mcp.mcp_server

The server communicates over stdio (standard input/output), which is the default MCP transport for IDE integration.

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IDE Integration

Cursor

Add to your Cursor MCP settings (.cursor/mcp.json in your project, or global settings):

{
  "mcpServers": {
    "greencode-mcp": {
      "command": "python",
      "args": ["-m", "src.greencode_mcp.mcp_server"],
      "cwd": "/absolute/path/to/GreenCodeMCP"
    }
  }
}

Then in Cursor's chat, the 9 GreenCodeMCP tools become available to the AI agent.

VS Code (with Copilot MCP support)

Add to your VS Code settings (.vscode/mcp.json):

{
  "servers": {
    "greencode-mcp": {
      "command": "python",
      "args": ["-m", "src.greencode_mcp.mcp_server"],
      "cwd": "/absolute/path/to/GreenCodeMCP"
    }
  }
}

Windsurf

Add to your Windsurf MCP configuration (~/.codeium/windsurf/mcp_config.json):

{
  "mcpServers": {
    "greencode-mcp": {
      "command": "python",
      "args": ["-m", "src.greencode_mcp.mcp_server"],
      "cwd": "/absolute/path/to/GreenCodeMCP"
    }
  }
}

Tips

• Replace /absolute/path/to/GreenCodeMCP with the actual absolute path to this repository.
• On Windows, use the full path to the Python executable inside the venv:

  "command": "C:/path/to/GreenCodeMCP/.venv/Scripts/python.exe"
  

• After configuring, restart the IDE or reload MCP servers.
• Verify the tools are loaded by asking the agent: "List available MCP tools".

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MCP Tools

#	Tool	Description
1	analyze_code	Detect energy anti-patterns (20 AST rules)
2	retrieve_green_practices	Query KB for relevant before/after examples
3	suggest_refactoring	Generate optimized code (auto-fix + LLM)
4	validate_equivalence	Verify functional correctness preservation
5	benchmark_resource_gain	Measure time, memory, energy, CO2 gains
6	run_full_green_refactor_pipeline	End-to-end pipeline (recommended)
7	generate_green_report	Produce JSON or Markdown report
8	list_demo_workloads	List available demo scenarios
9	run_demo_workload	Run a complete demo scenario

Typical Usage (via AI agent in IDE)

"Analyze this Python file for energy anti-patterns and suggest an optimized version"

The agent will call run_full_green_refactor_pipeline, which orchestrates all stages and returns a full report.

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Demo

Run the built-in demo without IDE integration:

# Single workload
python scripts/run_demo.py --workload transaction_analytics

# All demo workloads
python scripts/run_demo.py --all

Available Demo Workloads

Workload	Category	Anti-patterns	Expected Gain
statistics_sorting	Statistics & Sorting	Multiple sorts, list in sum, string concat	20–50%
numerical_loops	Numerical Computation	Generator misuse, redundant computation	15–40%
transaction_analytics	Data Processing	Deep copy, repeated iteration, O(n²) grouping	30–60%

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Project Structure

GreenCodeMCP/
├── src/greencode_mcp/          # Core MCP tool package
│   ├── mcp_server.py           # MCP server entry point (9 tools)
│   ├── config.py               # Centralized configuration
│   ├── schemas.py              # Data schemas
│   ├── analysis/               # 20 AST-based energy anti-pattern rules
│   ├── kb/                     # TF-IDF retrieval over 800-entry KB
│   ├── generation/             # Refactoring (auto-fix + LLM)
│   ├── validation/             # 4-level functional equivalence gate
│   ├── benchmark/              # Controlled benchmarking (CodeCarbon)
│   ├── pipelines/              # End-to-end pipeline orchestration
│   └── reporting/              # JSON + Markdown report generation
├── data/
│   ├── knowledge_base/         # 800 curated energy-optimization samples
│   └── demo_workloads/         # 3 controlled demo scenarios
├── scripts/
│   └── run_demo.py             # CLI demo runner
├── tests/                      # Test suite (91 tests)
├── pyproject.toml              # Package metadata and dependencies
├── requirements.txt            # Python dependencies
├── .env.example                # Environment variable template
└── LICENSE                     # MIT License

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Knowledge Base

The data/knowledge_base/ directory contains 800 curated Python energy-optimization examples derived from 7,056 candidates through a 10-step filtering pipeline. Each entry includes:

• Original (inefficient) code
• Optimized code
• Functional test assertions
• CodeCarbon-estimated energy measurements (before/after)
• Quality tier (Gold / Silver / Bronze)

Sources: MBPP, Mercury (NeurIPS 2024), HumanEval.

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Configuration

All parameters are configurable via environment variables or .env file:

Variable	Default	Description
NVIDIA_API_KEY	(empty)	API key for LLM-assisted refactoring
LLM_MODEL	qwen/qwen2.5-coder-32b-instruct	LLM model identifier
LLM_BASE_URL	https://integrate.api.nvidia.com/v1	LLM API endpoint
BENCHMARK_WARMUP	3	Warm-up iterations before measurement
BENCHMARK_RUNS	10	Number of measurement runs
BENCHMARK_TIMEOUT	60	Timeout per benchmark (seconds)
LOG_LEVEL	INFO	Logging verbosity

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Running Tests

pytest tests/ -v

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Limitations

• Energy/CO2 values are software-level estimates (CodeCarbon), not hardware measurements
• Validation depends on the completeness of available tests
• Currently supports Python only
• LLM-assisted refactoring requires internet access and API key
• The 20 AST rules detect local, single-file patterns only

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Citation

If you use GreenCodeMCP in your research, please cite:

@inproceedings{greencodemcp2026,
  title     = {GreenCodeMCP: An MCP-based Tool for Sustainable Software 
               Maintenance and Resource-Aware Refactoring},
  author    = {Oubelmouh, Youssef and Daoudi, Amir Salah Eddine and Chhieng, Pierre},
  booktitle = {Proceedings of the 42nd IEEE International Conference on 
               Software Maintenance and Evolution (ICSME), Tool Demonstration Track},
  year      = {2026}
}

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License

MIT — see LICENSE.