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Mallory MCP Server

Official
by malloryai

Mallory MCP Server

Mallory provides a robust source of cyber and threat intelligence. Use this MCP Server to enable your agents with real-time cyber threat intelligence and detailed information about vulnerabilities, threat actors, malware, techniques and other cyber-relevant entities and content.

📋 Prerequisites

  • Python 3.13 or higher
  • uv for dependency management (recommended)

🚀 Quick Start

Installation

Clone the repository:

git clone https://github.com/malloryai/mallory-mcp-server.git cd mallory-mcp-server

Set up a virtual environment and install dependencies:

# Using uv (recommended) uv venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate uv pip install -e . # Or using pip python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate pip install -e .

Install Development Dependencies

For development work, install the optional dependencies:

# Using uv uv pip install -e ".[lint,tools]" # Or using pip pip install -e ".[lint,tools]"

Set Up Pre-commit Hooks

This project uses pre-commit hooks to ensure code quality. Install them with:

pre-commit install ./scripts/install-commit-hook.sh

⚙️ Configuration

Create a .env file in the project root with the following variables:

APP_ENV=local MALLORY_API_KEY=your_api_key_here

🏃‍♂️ Running the Server

Direct Execution

python -m malloryai.mcp.app

or

uv run malloryai/mcp/app.py

Via the Claude Desktop Configuration

Add the following to your claude_desktop_config.json:

{ "mcpServers": { "MalloryAI": { "command": "/path/to/uv", "args": [ "run", "--python", "/path/to/mcp-server/.venv/bin/python", "/path/to/mcp-server/malloryai/mcp/app.py" ], "env": { "MALLORY_API_KEY": "your_api_key_here" } } } }

📦 Project Structure

. ├── README.md ├── app.py # Main application entry point ├── malloryai/ # Main package │ ├── __init__.py │ └── mcp/ # MCP subpackage │ ├── __init__.py │ ├── config/ # Configuration modules │ ├── server/ # Server implementation │ ├── tools/ # Tool implementations │ └── utils/ # Utility functions ├── pyproject.toml # Project metadata and dependencies ├── scripts/ # Utility scripts │ └── install-commit-hook.sh

🧪 Development

Code Style

This project uses:

Format your code with:

black . isort . flake8

Commit Message Format

This project follows the conventional commit format. Each commit message should follow this pattern:

<type>[(scope)]: <description>

Where type is one of:

  • feat or feature: New feature
  • fix, bugfix, or hotfix: Bug fixes
  • chore: Regular maintenance tasks
  • refactor: Code changes that neither fix bugs nor add features
  • docs: Documentation only changes
  • style: Changes that don't affect the meaning of the code
  • test: Adding or correcting tests
  • perf: Performance improvements
  • ci: Changes to CI configuration
  • build: Changes to build system or dependencies
  • revert: Reverting previous commits

Example: feat(server): add new authentication method

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'feat: add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request
-
security - not tested
-
license - not tested
-
quality - not tested

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

A robust Model Control Protocol server that enables AI agents to access real-time cyber threat intelligence and detailed information about vulnerabilities, threat actors, malware, and other cyber-security entities.

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