bloodhound_mcp

A Model Context Protocol (MCP) server that connects LLMs to BloodHound Community Edition and BloodHound Enterprise. Ask questions in natural language, get attack path analysis, run Cypher queries, and explore Active Directory, Azure/Entra ID, and OpenGraph environments — all from your AI assistant.

Watch the demonstration video

The server exposes BloodHound CE's REST API and Neo4j graph through a set of 13 composite MCP tools, 10 reference resources, and a system prompt tuned for offensive security analysis.

Each tool uses an info_type parameter to select what data is returned, keeping the tool surface small and token-efficient:

Reference material the LLM loads on demand — no extra API calls:

The bloodhound_assistant prompt includes behavioral rules that guide the LLM:

• Load the offensive query library before writing Cypher for any attack scenario
• Never draw privilege conclusions without checking group memberships and admincount
• Respect BloodHound's property naming conventions (hasspn, enabled, admincount — all lowercase)
• Handle uppercase name storage (DOMAIN ADMINS@CORP.LOCAL) correctly in filters
• Follow proper DCSync and GPO edge traversal patterns

• Python 3.11+
• uv
• BloodHound Community Edition instance with data loaded
• BloodHound API credentials (Token ID + Token Key)

git clone https://github.com/mwnickerson/bloodhound_mcp.git
cd bloodhound-mcp
uv sync

Create a .env file in the project root:

BLOODHOUND_DOMAIN=your-bloodhound-instance.domain.com
BLOODHOUND_TOKEN_ID=your-token-id
BLOODHOUND_TOKEN_KEY=your-token-key

The server defaults to https on port 443. Override if needed:

BLOODHOUND_PORT=8080
BLOODHOUND_SCHEME=http

TLS certificate verification remains enabled when no additional setting is provided. For a trusted lab deployment that uses a self-signed certificate, verification can be explicitly disabled:

BLOODHOUND_VERIFY_TLS=false

Disabling verification weakens transport security and logs a warning. Do not use this option on untrusted networks.

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "bloodhound_mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/bloodhound-mcp",
        "run",
        "main.py"
      ]
    }
  }
}

Add to ~/.claude/mcp.json:

{
  "mcpServers": {
    "bloodhound_mcp": {
      "type": "stdio",
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/bloodhound-mcp",
        "run",
        "main.py"
      ]
    }
  }
}

Add to ~/.codex/config.toml (or .codex/config.toml for project-scoped config):

[mcp_servers.bloodhound_mcp]
command = "uv"
args = ["--directory", "/path/to/bloodhound-mcp", "run", "main.py"]

Since the server loads credentials from .env automatically, no env block is needed. If you prefer to pass them explicitly:

[mcp_servers.bloodhound_mcp]
command = "uv"
args = ["--directory", "/path/to/bloodhound-mcp", "run", "main.py"]

[mcp_servers.bloodhound_mcp.env]
BLOODHOUND_DOMAIN = "your-bloodhound-instance.domain.com"
BLOODHOUND_TOKEN_ID = "your-token-id"
BLOODHOUND_TOKEN_KEY = "your-token-key"

• Command: uv
• Args: --directory /path/to/bloodhound-mcp run main.py

1. Log into BloodHound CE or BloodHound Enterprise
2. Navigate to Administration → API Tokens
3. Create a new token and copy the Token ID and Token Key into your .env

Reconnaissance:

What domains are in BloodHound?
Show me all Domain Admins in CORP.LOCAL
Find all kerberoastable users
Which computers have unconstrained delegation?

User and Group Analysis:

What admin rights does jsmith@corp.local have?
Show me all sessions for the administrator account
What groups is this user a member of?
Who controls the IT ADMINS group?

Attack Path Analysis:

Find the shortest path from jsmith@corp.local to Domain Admins
Who has DCSync rights in the domain?
Show me all GPO abuse paths
Find ADCS ESC1 paths in the domain

Custom Cypher:

Run a Cypher query to find all users with SPN set and admincount=1
Find all computers where DOMAIN USERS can RDP

Collection Uploads:

Upload this SharpHound ZIP from /tmp/sharphound.zip into BloodHound
Upload these base64-encoded SharpHound ZIP bytes as sharphound.zip
Start an upload job, upload these base64 JSON bytes as users.json, then end the job

Agents that already hold a SharpHound or AzureHound collection in memory should base64-encode the collection bytes and call:

file_upload(
    info_type="upload_bytes",
    file_name="sharphound.zip",
    file_bytes_base64="<base64-encoded zip bytes>"
)

For multi-file jobs, call start_job, then upload_bytes_to_job for each base64 payload, then end_job.

BloodHound 8.0+ supports custom node types via OpenGraph, letting you model non-AD infrastructure (cloud resources, databases, custom assets) in the same graph as Active Directory.

The custom_nodes tool handles legacy CRUD operations on node type display configurations through /api/v2/custom-nodes. For BloodHound v9.0.0+ instances with OpenGraph extension management enabled, the same composite tool also supports /api/v2/extensions and /api/v2/extensions-edges via extension_list, extension_upsert, extension_delete, and extension_edges.

Use the bloodhound://opengraph/guide and bloodhound://opengraph/examples resources for schema design and Cypher patterns. For structured OpenGraph schemas, upsert the extension schema first, then ingest collection data with file_upload.

Requires BloodHound Enterprise or BloodHound CE 8.0 or later. OpenGraph extension management requires BloodHound 9.0.0+ and the corresponding feature flag to be enabled.

BloodHound data processed through this tool is transmitted to your LLM provider's servers. Do not use this with production AD data unless you have assessed that risk.

Recommended use cases:

• Lab environments (GOAD, DetectionLab, custom ranges)
• Training and certification prep
• Research and tool development
• Non-production domain analysis

Best practices:

• Rotate BloodHound API tokens regularly
• Use a read-only API token where possible
• Consider a local LLM bridge for sensitive environments

# Full test suite
uv run pytest

# Specific modules
uv run pytest tests/test_main_mcp_tools.py -v
uv run pytest tests/test_bloodhound_api.py -v

# Integration tests (requires a live BloodHound instance)
BLOODHOUND_INTEGRATION_TESTS=1 uv run pytest tests/test_integration.py -v

• Direct Neo4j access mode (bypass REST API for complex graph traversal)
• Enhanced Azure/Entra ID tooling
• Improved ADCS attack path coverage
• Additional OpenGraph examples and templates

Contributions are welcome. Open an issue to discuss significant changes before submitting a PR.

1. Fork the repo
2. Create a feature branch
3. Add tests for new functionality
4. Run uv run pytest and confirm everything passes
5. Submit a pull request

• SpecterOps for BloodHound Community Edition
• Orange Cyberdefense for GOAD (used for testing)
• @jlowin for FastMCP
• @xpn for MCP inspiration via the Mythic MCP project

GNU General Public License v3.0 — see LICENSE for details.