socfortress/velociraptor-mcp-server

Velociraptor MCP Server

A production-ready Model Context Protocol (MCP) server for seamless integration between Velociraptor DFIR and Large Language Models (LLMs).

Why? Combine the power of Velociraptor's comprehensive digital forensics and incident response capabilities with the reasoning capabilities of large language models—enabling natural language queries and intelligent analysis of your forensic data.

✨ Key Features

• 🚀 Production-ready: Proper package structure, logging, error handling, and configuration management
• 🔐 Secure: JWT token management with automatic refresh
• 🌐 HTTP/2 Support: Built on modern async HTTP client with connection pooling
• 📊 Comprehensive API: Access Velociraptor artifacts, hunts, collections, and more
• 🎛️ Configurable: Environment variables, CLI arguments, and fine-grained tool filtering
• 📦 Pip installable: Install directly from GitHub releases or source

Table of Contents

• Quick Start
• Installation
• Configuration
• Usage
• Available Tools
• Development
• CI/CD
• Deployment
• Security
• Contributing
• License

Quick Start

1. Install

From GitHub (Recommended)

python -m venv .venv && source .venv/bin/activate
pip install git+https://github.com/socfortress/velociraptor-mcp-server.git

From Release Artifacts

1. Go to the Releases page
2. Download the latest .whl file
3. Install with:

pip install velociraptor_mcp_server-*.whl

From Build Artifacts (Latest Build)

1. Go to the Actions tab
2. Click on the latest successful workflow run
3. Download the python-package-distributions artifact
4. Extract and install:

pip install velociraptor_mcp_server-*.whl

2. Configure Environment

Create a .env file in your project directory:

# Velociraptor Server Configuration
VELOCIRAPTOR_API_KEY=/path/to/api.config.yaml
VELOCIRAPTOR_SSL_VERIFY=false
VELOCIRAPTOR_TIMEOUT=30

# MCP Server Configuration
MCP_SERVER_HOST=127.0.0.1
MCP_SERVER_PORT=8000

# Logging Configuration
LOG_LEVEL=INFO

# Tool Filtering (optional)
# VELOCIRAPTOR_DISABLED_TOOLS=CollectArtifactTool,RunVQLQueryTool

Note: For VELOCIRAPTOR_API_KEY, provide the full path to your Velociraptor api.config.yaml file. You can generate this file from your Velociraptor server using the admin interface or CLI.

3. Run the Server

# Using the CLI command
velociraptor-mcp-server

# Or using the Python module
python -m velociraptor_mcp_server

# With custom configuration
velociraptor-mcp-server --host 0.0.0.0 --port 8080 --log-level DEBUG

The server will start and be available at http://127.0.0.1:8000 (or your configured host/port).

Installation

Requirements

• Python 3.11 or higher
• Access to a Velociraptor server instance
• Network connectivity to your Velociraptor server
• Velociraptor API configuration file (api.config.yaml)

Development Installation

git clone https://github.com/socfortress/velociraptor-mcp-server.git
cd velociraptor-mcp-server

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

# Install pre-commit hooks (optional)
pre-commit install

Configuration

Environment Variables

CLI Options

velociraptor-mcp-server --help

Available options:

• --host: Host to bind server to (default: 127.0.0.1)
• --port: Port to bind server to (default: 8000)
• --log-level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
• --config: Path to Velociraptor API config file (overrides env var)
• --no-ssl-verify: Disable SSL certificate verification

Usage

Basic Usage

from velociraptor_mcp_server import Config, create_server

# Create server with environment configuration
config = Config.from_env()
server = create_server(config)

# Start the server
server.start()

Custom Configuration

from velociraptor_mcp_server.config import VelociraptorConfig, ServerConfig, Config

# Create custom configuration
velociraptor_config = VelociraptorConfig(
    api_key="/path/to/api.config.yaml",
    ssl_verify=False,
    timeout=60
)

server_config = ServerConfig(
    host="0.0.0.0",
    port=8080,
    log_level="DEBUG"
)

config = Config(velociraptor=velociraptor_config, server=server_config)
server = create_server(config)

Integration with LangChain

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langchain.agents import AgentType, initialize_agent

# Initialize LLM
model = ChatOpenAI(model="gpt-4")

# Connect to Velociraptor MCP server
client = MultiServerMCPClient({
    "velociraptor-mcp-server": {
        "transport": "sse",
        "url": "http://127.0.0.1:8000/sse/",
    }
})

# Get tools and create agent
tools = await client.get_tools()
agent = initialize_agent(
    tools=tools,
    llm=model,
    agent=AgentType.OPENAI_FUNCTIONS,
    verbose=True
)

# Query your Velociraptor data
response = await agent.ainvoke({
    "input": "Show me all active Velociraptor clients and their OS information"
})

# Collect artifacts from a specific client
artifact_response = await agent.ainvoke({
    "input": "Collect Windows.System.Users artifact from client workstation-01"
})

# Get artifact collection results
results_response = await agent.ainvoke({
    "input": "Get the results from flow F.ABC123 for Windows.System.Users artifact"
})

Available Tools

The server exposes the following MCP tools for Velociraptor integration:

1. AuthenticateTool

• Purpose: Initialize and test connection to Velociraptor server
• Parameters: None
• Usage: Establishes a gRPC connection using the api.config.yaml file and tests authentication
• Example:

  {"args": {}}
  

2. GetAgentInfo

• Purpose: Retrieve detailed information about a Velociraptor client by hostname or FQDN
• Parameters:
  • hostname (required): Hostname or FQDN of the client to search for
• Usage: Searches for a client and returns comprehensive details including client ID, OS information, agent version, and connection status
• Example:

  {"args": {"hostname": "workstation-01"}}
  {"args": {"hostname": "server.domain.com"}}
  

3. RunVQLQueryTool

• Purpose: Execute VQL (Velociraptor Query Language) queries on the Velociraptor server
• Parameters:
  • vql (required): VQL query string to execute
  • max_rows (optional): Maximum number of rows to return
  • timeout (optional): Query timeout in seconds
• Usage: Allows custom VQL queries to retrieve information about clients, artifacts, hunts, flows, and more
• Examples:

  {"args": {"vql": "SELECT client_id, os_info.hostname FROM clients() LIMIT 10"}}
  {"args": {"vql": "SELECT * FROM flows() WHERE client_id = 'C.1234567890'"}}
  {"args": {"vql": "SELECT name, description FROM artifacts() WHERE name =~ 'Windows'"}}
  

4. ListLinuxArtifactsTool

• Purpose: List available Linux artifacts in Velociraptor
• Parameters: None
• Usage: Returns a summary of all Linux client artifacts including names, descriptions, and required parameters
• Example:

  {"args": {}}
  

5. ListWindowsArtifactsTool

• Purpose: List available Windows artifacts in Velociraptor
• Parameters: None
• Usage: Returns a summary of all Windows client artifacts including names, descriptions, and required parameters. Includes performance notes for NTFS queries (MFT, USN) and path filtering recommendations.
• Example:

  {"args": {}}
  

6. CollectArtifactTool

• Purpose: Collect a Velociraptor artifact from a client
• Parameters:
  • client_id (required): Velociraptor client ID to target for collection
  • artifact (required): Name of the Velociraptor artifact to collect
  • parameters (optional): Comma-separated string of key='value' pairs to pass to the artifact
• Usage: Initiates artifact collection on a target client and returns a flow_id for tracking
• Examples:

  {"args": {"client_id": "C.1234567890", "artifact": "Windows.System.Users"}}
  {"args": {"client_id": "C.0987654321", "artifact": "Linux.System.Uptime", "parameters": "format='seconds'"}}
  

7. GetCollectionResultsTool

• Purpose: Retrieve Velociraptor collection results for a given client, flow ID, and artifact
• Parameters:
  • client_id (required): Velociraptor client ID where the collection was run
  • flow_id (required): Flow ID returned from the initial collection
  • artifact (required): Name of the artifact collected (e.g., Windows.NTFS.MFT)
  • fields (optional): Comma-separated string of fields to return (default: '*')
  • max_retries (optional): Number of times to retry if the flow hasn't finished (default: 5)
  • retry_delay (optional): Time in seconds to wait between retries (default: 5)
• Usage: Waits and retries if the flow hasn't finished or if no results are immediately available. Supports partial results for multi-source artifacts.
• Features:
  • Multi-source support: Handles artifacts with multiple sources (e.g., Linux.Debian.Packages with DebPackages/Snaps)
  • Partial results: Returns completed sources even if others are still running
  • Intelligent retry: Automatically waits for collection completion
• Examples:

  {"args": {"client_id": "C.1234567890", "flow_id": "F.ABC123", "artifact": "Windows.System.Users"}}
  {"args": {"client_id": "C.0987654321", "flow_id": "F.DEF456", "artifact": "Linux.System.Uptime", "fields": "Uptime,BootTime"}}
  

8. CollectArtifactDetailsTool

• Purpose: Get detailed information about a specific Velociraptor artifact
• Parameters:
  • artifact_name (required): Name of the artifact to get details for
• Usage: Retrieves comprehensive details including description, parameters, source code/VQL, parameter types, and default values. Useful for understanding artifacts before collection or debugging issues.
• Examples:

  {"args": {"artifact_name": "Windows.System.Users"}}
  {"args": {"artifact_name": "Linux.Network.Netstat"}}
  {"args": {"artifact_name": "Windows.NTFS.MFT"}}
  

9. ListLinuxArtifactNamesTool

• Purpose: List only the names of available Linux artifacts in Velociraptor
• Parameters: None
• Usage: Returns a simple list of artifact names for Linux client artifacts
• Example:

  {"args": {}}
  

10. ListWindowsArtifactNamesTool

• Purpose: List only the names of available Windows artifacts in Velociraptor
• Parameters: None
• Usage: Returns a simple list of artifact names for Windows client artifacts
• Example:

  {"args": {}}
  

11. FindArtifactDetailsTool

• Purpose: Find a Velociraptor artifact's name, description, and parameters by artifact name
• Parameters:
  • artifact_name (required): Name of the artifact to get details for
• Usage: Returns a summary for the specified artifact, including its name, description, and parameter list
• Example:

  {"args": {"artifact_name": "Windows.RemoteDesktop.RDPConnections"}}
  

Artifact Collection Workflow

The tools work together to provide a complete artifact collection workflow:

1. Discovery: Use ListLinuxArtifactsTool or ListWindowsArtifactsTool to explore available artifacts
2. Investigation: Use CollectArtifactDetailsTool to understand artifact parameters and requirements
3. Client Identification: Use GetAgentInfo to find the target client by hostname
4. Collection: Use CollectArtifactTool to start artifact collection and get a flow_id
5. Results: Use GetCollectionResultsTool to monitor progress and retrieve results
6. Custom Queries: Use RunVQLQueryTool for advanced custom investigations

Development

Setting up Development Environment

git clone https://github.com/socfortress/velociraptor-mcp-server.git
cd velociraptor-mcp-server

# Create virtual environment
python -m venv .venv
source .venv/bin/activate

# Install in development mode with dev dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=velociraptor_mcp_server

# Run specific test file
pytest tests/test_client.py

# Run with verbose output
pytest -v

Building the Package

# Install build dependencies
pip install build twine

# Build the package
python -m build

# Check the package
twine check dist/*

# Test installation
pip install dist/*.whl

Continuous Integration

This project uses GitHub Actions for automated building and testing:

• Automatic builds: Every push to main and develop branches triggers a build
• Quality assurance: Comprehensive testing including linting, type checking, and unit tests
• Artifact publishing: Built packages are available as GitHub releases and workflow artifacts

Creating a Release

1. Create and push a git tag:

   git tag v1.0.0
   git push origin v1.0.0
   

2. The GitHub Action will automatically:

   • Build the package
   • Run all tests
   • Create a GitHub release with downloadable artifacts

Installing from CI Artifacts

Visit the Actions page and download the python-package-distributions artifact from any successful build.

Security Considerations

Credentials Management

• Never commit credentials: Use environment variables or secrets management
• Secure API config: Protect your Velociraptor API configuration file (api.config.yaml)
• Certificate security: Ensure proper handling of client certificates and private keys

Network Security

• TLS/SSL: Always use SSL/TLS for gRPC connections (VELOCIRAPTOR_SSL_VERIFY=true)
• Firewall rules: Restrict access to necessary ports only
• VPN/Private networks: Deploy in secured network environments

Access Control

• Least privilege: Use Velociraptor API keys with minimal required permissions
• Tool filtering: Disable unnecessary tools using VELOCIRAPTOR_DISABLED_TOOLS
• Client access: Restrict which clients can be accessed through the API

Monitoring

• Logging: Enable appropriate log levels for monitoring
• Health checks: Implement monitoring of the MCP server endpoint
• Rate limiting: Consider implementing rate limiting for production deployments

Project Structure

velociraptor-mcp-server/
├── velociraptor_mcp_server/          # Main package
│   ├── __init__.py           # Package initialization
│   ├── __main__.py           # CLI entry point
│   ├── client.py             # Velociraptor API client
│   ├── config.py             # Configuration management
│   ├── server.py             # MCP server implementation
│   └── exceptions.py         # Custom exceptions
├── tests/                    # Test suite
│   ├── conftest.py          # Test configuration
│   ├── test_client.py       # Client tests
│   ├── test_config.py       # Configuration tests
│   └── test_server.py       # Server tests
├── .github/workflows/        # GitHub Actions
│   └── publish.yml          # CI/CD pipeline
├── requirements.txt          # Dependencies
├── pyproject.toml           # Package configuration
├── .env.example             # Environment template
├── Dockerfile               # Docker configuration
└── README.md                # This file

Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes and add tests
4. Ensure tests pass: pytest
5. Check code quality: black ., isort ., flake8 .
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to your branch (git push origin feature/amazing-feature)
8. Open a Pull Request

Development Guidelines

• Write tests for new functionality
• Follow existing code style and patterns
• Update documentation for new features
• Ensure all CI checks pass

License

This project is licensed under the MIT License - see the file for details.

Changelog

v0.1.0 (Latest)

• Initial release
• Basic MCP server functionality
• Velociraptor API integration with gRPC authentication
• Complete artifact collection workflow
• CLI interface with configuration options
• Comprehensive test suite
• GitHub Actions CI/CD pipeline
• Support for multi-source artifacts with partial results

Support

• 📖 Documentation
• 🐛 Issues
• 🏢 SOCFortress

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