Vectra AI MCP Server

This project implements an MCP server for the Vectra AI Platform.

What is Vectra AI MCP?

An MCP server that connects AI assistants to your Vectra AI security platform, enabling intelligent analysis of threat detection data, security insights, and automated incident response workflows. Compatible with Claude, ChatGPT, Cursor, VS Code and other MCP-enabled AI tools.

What can you do with Vectra AI MCP?

• Investigate threats in natural language
• Take response actions in Vectra directly from your AI agent
• Correlate and analyze security data using prompts
• Dynamically build advanced visulizations for analysis
• Generate investigation reports from natural language
• Retrieve complete platform health or a specific health category, including EDR, external connector, and Network Brain status

Setup - uvx (Recommended for End Users)

uvx (part of uv) runs the published package in an isolated, ephemeral environment — no clone, no virtualenv, no Docker required. This is the easiest way to use the server when you just want to plug it into Claude Desktop, Cursor, VS Code, or any other MCP client.

Prerequisites

• uv installed:

  # macOS / Linux
  curl -LsSf https://astral.sh/uv/install.sh | sh
  
  # Windows
  powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
  
  # Or via pip
  pip install uv
  

• Python 3.12+ is fetched automatically by uvx if it’s not on your system.

Quick check

Once uv is installed, verify everything works without installing anything permanently:

# Once published to PyPI:
uvx vectra-ai-mcp-server --help

# Until then (or to track the latest main branch):
uvx --from git+https://github.com/vectra-ai-research/vectra-ai-mcp-server vectra-ai-mcp-server --help

MCP client configuration (single tenant)

Pass credentials via the env block of your MCP client config — uvx does not load .env for you.

{
  "mcpServers": {
    "vectra-ai-mcp": {
      "command": "uvx",
      "args": ["vectra-ai-mcp-server"],
      "env": {
        "VECTRA_BASE_URL": "https://123456789.ab1.portal.vectra.ai",
        "VECTRA_CLIENT_ID": "",
        "VECTRA_CLIENT_SECRET": ""
      }
    }
  }
}

Pin a specific version with uvx [email protected] (recommended for production setups).

MCP client configuration (multi-tenant)

Provide an absolute path to a tenants.yaml file you’ve placed somewhere on disk (e.g. ~/.config/vectra/tenants.yaml):

{
  "mcpServers": {
    "vectra-ai-mcp": {
      "command": "uvx",
      "args": [
        "vectra-ai-mcp-server",
        "--config",
        "/absolute/path/to/tenants.yaml"
      ],
      "env": {