bloodhound_mcp: A Model Context Protocol (MCP) server to converse with data in Bloodhound

by mwnickerson · MCP Server · ★ 121

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install bloodhound_mcp

git clone https://github.com/mwnickerson/bloodhound_mcp.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 77/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars121
Forks28
LanguagePython
CategoryMCP Server
LicenseGPL-3.0
Quality Score77/100
Last Updated2026-08-19
Created2025-03-22
Platformsmcp, python
Est. Tokens~15k

About bloodhound_mcp

BloodHound MCP A Model Context Protocol (MCP) server that connects LLMs to BloodHound Community Edition. 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.

Compatible Skills

These tools work well together with bloodhound_mcp for enhanced workflows:

  • plane-mcp-server — semantic(0.70)+same_lang+similar_pop+shared_platform (60%)
  • data-agent-skill — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (58%)
  • dbt-mcp — semantic(0.60)+same_lang+similar_pop+shared_platform (56%)
  • Easy-MCP-AutoCad — semantic(0.81)+same_lang+similar_pop+shared_platform (55%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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