Velociraptor

Velociraptor MCP is a POC Model Context Protocol bridge for exposing LLMs to MCP clients.

Initial version has several Windows orientated triage tools deployed. Best use is querying usecase to target machine name.

e.g

can you give me all network connections on MACHINENAME and look for suspicious processes?

can you tell me which artifacts target the USN journal

Installation

1. Setup an API account

https://docs.velociraptor.app/docs/server_automation/server_api/

Generate an api config file:

velociraptor --config /etc/velociraptor/server.config.yaml config api_client --name api --role administrator,api api_client.yaml

2. Clone mcp-velociraptor repo and test API

• Copy api_client.yaml to the repo root, or keep it anywhere local and set VELOCIRAPTOR_API_CONFIG=/path/to/api_client.yaml.
• Copy example.env to .env for local development, then set VELOCIRAPTOR_API_CONFIG to your real api_client.yaml path. The bridge, smoke script, and agent load dotenv config automatically without overriding variables already supplied by your shell or MCP client.
• api_client.yaml is gitignored and should not be committed.
• Run .venv/bin/python test_api.py to confirm the API works when .env is configured, or use VELOCIRAPTOR_API_CONFIG=/path/to/api_client.yaml .venv/bin/python test_api.py to override it for a single run.
• The MCP bridge reads the same VELOCIRAPTOR_API_CONFIG environment variable after loading dotenv config.
• For multi-tenant deployments, optionally set VELOCIRAPTOR_ORG_ID to choose a default org context.
• Set VELOCIRAPTOR_DEBUG_VQL=1 only when you want raw VQL request logging on stderr for debugging.
• Set ENABLE_DANGEROUS_TOOLS=true only when you explicitly want to enable raw VQL, quarantine, and remote process-kill tools.
• The agent POC defaults to local Ollama summaries. Set VELOCIRAPTOR_MODEL_PROVIDER=azure, AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, and AZURE_OPENAI_MODEL when you explicitly want Azure OpenAI summaries.

3. Connect to MCP client of choice

The easiest configuration is to run your venv python directly calling mcp_velociraptor_bridge.py. You can either put values directly in the MCP client config:

{
  "mcpServers": {
    "velociraptor": {
      "command": "/path/to/venv/bin/python",
      "env": {
        "VELOCIRAPTOR_API_CONFIG": "/path/to/api_client.yaml",
        "VELOCIRAPTOR_ORG_ID": "O123"
      },
      "args": [
        "/path/to/mcp_velociraptor_bridge.py"
      ]
    }
  }
}

Or point the MCP client at a dotenv file:

{
  "mcpServers": {
    "velociraptor": {
      "command": "/path/to/venv/bin/python",
      "env": {
        "VELOCIRAPTOR_ENV_FILE": "/path/to/mcp-velociraptor/.env"
      },
      "args": [
        "/path/to/mcp_velociraptor_bridge.py"
      ]
    }
  }
}

Config precedence is: direct environment values from the MCP client or shell, then VELOCIRAPTOR_ENV_FILE if set, then repo-local .env, then fallback paths such as ./api_client.yaml and ~/.config/api_client.yaml.

The separate agent proof-of-concept now lives under agent_poc/. It now includes an engagement manager that fans out to isolated, dynamically allowlisted analysts in parallel. The engagement profile runs bounded process, network, persistence, execution, user-activity, and system-inventory roles; deep also opts into heavier filesystem and security roles. Evidence collection is deterministic and synthesis is model-only per role. It supports local Ollama summaries by default and opt-in Azure OpenAI API summaries via environment configuration. Azure OpenAI mode sends collected evidence bundles to the configured Azure API account for summarization. See agent_poc/README.md for agent-specific usage and automation examples, including verbose collection progress output with artifact names and row counts.

4. Tool Response Format

MCP tool responses are emitted as JSON text envelopes so stdio clients do not need to parse Python repr() output:

{"ok": true, "data": {...}}

or

{"ok": false, "error": "message"}

collect_artifact and hunt_across_fleet accept parameters as a structured JSON object with scalar values or lists of scalar values, for example {"PathRegex": ".*", "Targets": ["_BasicCollection"]}. Legacy compatibility input can be supplied via legacy_parameters with simple scalar assignments or list literals such as PathRegex='.*',Targets=['_BasicCollection']; raw VQL fragments are no longer passed through. Parameterized hunts are validated against Velociraptor's compiled collector args; if the requested artifact env does not land, the hunt is stopped and the tool returns an error. collect_forensic_triage wraps Windows.Triage.Targets with Targets='["_BasicCollection"]' and a collection timeout of 2400 seconds.

5. Tool Inventory

The bridge now exposes the original Windows/Linux triage tools plus expanded fleet, Linux, macOS, Windows, YARA, and response helpers:

• Fleet: list_orgs, client_info, list_clients, hunt_across_fleet, get_hunt_results_tool, run_vql.
• Artifact discovery: list_windows_artifacts, list_linux_artifacts, and list_macos_artifacts accept name_regex filters and include_parameter_details=true for full live parameter metadata from artifact_definitions().
• Linux: process, group, mount, network, users, crontab, services, SSH keys/logins, shell history, last login, ARP cache, journal search, file finder, and YARA process scanning.
• macOS: process, users, network, LaunchAgents/Daemons, login items, shell history, browser history, quarantine events, TCC database, file finder, and artifact discovery.
• Windows: process, network, scheduled tasks, services, RecentDocs, Shellbags, USB/mount evidence, execution traces, MFT, USN, SRUM, EVTX, PowerShell, autoruns, WMI persistence, RDP, DNS cache, Recycle Bin, browser history, memory/malfind/mutant checks, shadow copies, timestomp checks, and file/YARA hunting with optional hash calculation.
• Generic response/collection: collect_artifact, get_collection_results, collect_forensic_triage, collect_file, quarantine_host, unquarantine_host, and kill_process.

Thanks to @snoe-findley for sharing a fork that expanded available tools and some of the newer cross-platform additions.

6. Caveats

Due to the nature of DFIR, results depend on amount of data returned, model use and context window.

I have included a function to find artifacts and dynamically create collections but had mixed results. I have been pleasantly surprised with some results and disappointed when running other collections that cause lots of rows.

Check licencing - Anthropic's DPA is only tied to their Commercial Terms, which means that for client/production endpoint data you would need commercial licencing to leverage this MCP. Other MCP clients work just fine.

Please let me know how you go and feel free to add PR!

can you give me all network connections on MACHINENAME and look for suspicious processes?

can you tell me which artifacts target the USN journal