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mallory-mcp-server

MCP.Pizza Chef: malloryai

Mallory MCP Server is a specialized MCP server designed to provide real-time cyber threat intelligence to AI agents and applications. It exposes structured, up-to-date information about cybersecurity threats including vulnerabilities, threat actors, malware, and attack techniques. By integrating Mallory, developers can empower their AI models with actionable cyber intelligence, enabling enhanced security analysis, threat detection, and response automation. The server requires Python 3.13+ and supports dependency management via uv. Mallory is open source under the MIT license and is ideal for security-focused AI workflows that need continuous, detailed cyber threat context.

Use This MCP server To

Provide real-time cyber threat intelligence to AI agents Expose detailed vulnerability data for security analysis Supply information on threat actors and malware Enable AI-driven threat detection and response Integrate cyber intelligence into automated workflows Support security research with structured threat data

README

Mallory MCP Server

Python 3.13+ License: MIT smithery badge

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.

๐Ÿ“‹ Prerequisites

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

๐Ÿš€ 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

โš™๏ธ Configuration

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

APP_ENV=local
MALLORY_API_KEY=your_api_key_here

๐Ÿƒโ€โ™‚๏ธ 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"
      }
    }
  }
}

๐Ÿ“ฆ Project Structure

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

๐Ÿงช 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

๐Ÿค 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

mallory-mcp-server FAQ

How do I install Mallory MCP Server?
Clone the GitHub repo, set up a Python 3.13+ virtual environment, and install dependencies using uv or pip.
What prerequisites are needed to run Mallory MCP Server?
Python 3.13 or higher is required, along with uv for dependency management (recommended).
Is Mallory MCP Server open source?
Yes, it is licensed under the MIT license and available on GitHub.
Can Mallory MCP Server integrate with multiple LLM providers?
Yes, it is designed to work with any MCP client and supports models like OpenAI, Anthropic Claude, and Google Gemini.
What kind of cyber threat data does Mallory provide?
It provides detailed information on vulnerabilities, threat actors, malware, attack techniques, and other cyber-relevant entities.
How does Mallory MCP Server enhance AI security workflows?
By feeding real-time, structured cyber threat intelligence into AI models, it enables improved threat detection, analysis, and automated response.
Does Mallory MCP Server require special hardware?
No special hardware is required; it runs on standard Python 3.13+ environments.
How do I update the threat intelligence data in Mallory?
Updates depend on the data sources Mallory integrates; typically, it fetches the latest threat intel automatically or via configured refresh mechanisms.