Reversecore MCP逆向工程服务

Unit tests only (fast, no external dependencies)

pytest tests/unit/ -v

Integration tests (requires Docker)

pytest tests/integration/ -v

System Requirements

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Option 1 — Docker (Recommended)

The fastest way to get started with zero dependency installation:

docker run -i --rm \
  -v /path/to/your/samples:/app/workspace \
  -e REVERSECORE_WORKSPACE=/app/workspace \
  -e MCP_TRANSPORT=stdio \
  ghcr.io/sjkim1127/reversecore_mcp:latest

Option 2 — Build from Source

git clone https://github.com/sjkim1127/Reversecore_MCP.git
cd Reversecore_MCP
./scripts/run-docker.sh        # auto-detects Intel / Apple Silicon

Or manually:

docker compose --profile x86 up -d    # Intel/AMD
docker compose --profile arm64 up -d  # Apple Silicon (M1/M2/M3)

⚡ Option 1: Docker Exec Mode (Highly Recommended)

If you run the server via Docker Compose (in the background), this mode uses standard stdio channeled directly inside the running container. It offers zero startup latency, persistent analysis memory, and perfect compatibility (bypasses IDE client-side HTTP/SSE connection bugs).

{
  "mcpServers": {
    "Reversecore_MCP": {
      "command": "docker",
      "args": [
        "exec",
        "-i",
        "-e",
        "MCP_TRANSPORT=stdio",
        "reversecore-mcp-arm64",
        "python",
        "server.py"
      ]
    }
  }
}

Note: Replace reversecore-mcp-arm64 with reversecore-mcp if you are on an Intel/AMD architecture.

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📦 Option 2: Stdio Mode (Docker-on-Demand)

If you prefer running a fresh, isolated container automatically on every request:

<details> <summary>🍎 macOS</summary>

{
  "mcpServers": {
    "reversecore": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "/Users/YOUR_USERNAME/samples:/app/workspace",
        "-e", "REVERSECORE_WORKSPACE=/app/workspace",
        "-e", "MCP_TRANSPORT=stdio",
        "ghcr.io/sjkim1127/reversecore_mcp:latest"
      ]
    }
  }
}

</details>

<details> <summary>🐧 Linux</summary>

{
  "mcpServers": {
    "reversecore": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "/home/YOUR_USERNAME/samples:/app/workspace",
        "-e", "REVERSECORE_WORKSPACE=/app/workspace",
        "-e", "MCP_TRANSPORT=stdio",
        "ghcr.io/sjkim1127/reversecore_mcp:latest"
      ]
    }
  }
}

</details>

<details> <summary>🪟 Windows</summary>

{
  "mcpServers": {
    "reversecore": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "C:/samples:/app/workspace",
        "-e", "REVERSECORE_WORKSPACE=/app/workspace",
        "-e", "MCP_TRANSPORT=stdio",
        "ghcr.io/sjkim1127/reversecore_mcp:latest"
      ]
    }
  }
}

</details>

⚠️ Important — File Paths Inside Docker Your local folder is mounted to /app/workspace inside the container. Always reference files by filename only, not by your local full path. | ❌ Wrong | ✅ Correct | |---|---| | r2_decompile("/Users/john/samples/mal.exe") | r2_decompile("mal.exe") |

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Setup

git clone https://github.com/sjkim1127/Reversecore_MCP.git
cd Reversecore_MCP
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
pip install -r requirements-dev.txt
pre-commit install   # installs Gitleaks, Ruff, Bandit hooks

Guided Analysis Prompts

Activate expert analysis modes by referencing these prompts in your AI client:

How prompts work: Each prompt primes the AI with an expert persona, structured Chain-of-Thought checkpoints, and evidence classification (OBSERVED / INFERRED / POSSIBLE). This produces analyst-grade output, not just raw tool output.

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Quick Start

Option 3 — Python (Local Development)

git clone https://github.com/sjkim1127/Reversecore_MCP.git
cd Reversecore_MCP
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
python server.py

Prerequisites for local mode: Radare2 must be installed on your system (r2 --version). YARA is installed automatically via yara-python.

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🌐 Option 2: SSE HTTP Mode

If you prefer network-based streaming (Server-Sent Events) for remote clients:

{
  "mcpServers": {
    "Reversecore_MCP": {
      "url": "http://localhost:8000/mcp/sse"
    }
  }
}

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Configuration

All settings can be provided via environment variables or a .env file (see .env.example):

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🔗 Cross-Reference & Binary Annotation

Core Infrastructure Modules

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CI/CD Pipeline

Every push to main runs the following gates — all must pass before deployment:

Lint & Security Gate            Unit Tests (Python Matrix)
  ├─ Gitleaks (secret scan)       ├─ pytest 3.10 --cov-fail-under=80
  ├─ Hadolint (Dockerfile)        ├─ pytest 3.11 --cov-fail-under=80
  ├─ Ruff check + format          └─ pytest 3.12 --cov-fail-under=80
  ├─ Mypy type check (87 files)
  ├─ Bandit (all severities)    Docker Verification
  └─ pip-audit (zero CVE)         ├─ Build multi-arch image (amd64/arm64)
                                  ├─ Trivy container scan (LOW→CRITICAL)
CodeQL Analysis                   ├─ Integration tests (inside container)
  └─ Python SAST                  └─ E2E MCP tool invocation

Deploy (main branch only)
  └─ Push to GHCR + Trivy rescan on published image

Zero-bypass policy: CI/CD failures are never resolved by modifying pipeline configuration. Root causes are always fixed directly in source code or dependencies.

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