filesystem vs BloodHound-MCP-AI
Side-by-side comparison to help you pick between these two MCP servers.
filesystem by modelcontextprotocol | BloodHound-MCP-AI by MorDavid | |
|---|---|---|
| Stars | ★ 85,748 | ★ 353 |
| 30d uses | — | — |
| Score | 77 | 47 |
| Official | ✓ | — |
| Categories | File SystemDeveloper ToolsProductivity | SecurityKnowledge GraphDeveloper Tools |
| Language | TypeScript | Python |
| Last commit | this month | 12 mo ago |
filesystem · Summary
A feature-rich MCP server for filesystem operations with dynamic directory access control.
BloodHound-MCP-AI · Summary
MCP server connecting BloodHound with AI for natural language Active Directory security analysis.
filesystem · Use cases
- Enable AI models to read and write project files during development
- Allow Claude or other MCP clients to browse and analyze codebases
- Provide secure sandboxed access to specific directories for content generation
BloodHound-MCP-AI · Use cases
- Visualize and analyze Active Directory attack paths without knowing Cypher queries
- Assess AD security posture by identifying potential privilege escalation paths
- Generate comprehensive security reports for stakeholders using natural language
filesystem · Install
Installation
Using NPX
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/path/to/allowed/directory"
]
}
}
}Using Docker
{
"mcpServers": {
"filesystem": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"--mount", "type=bind,src=/path/to/allowed/dir,dst=/projects/allowed/dir",
"mcp/filesystem",
"/projects"
]
}
}
}VS Code Extension
Click the installation buttons in the README to install directly in VS Code.
BloodHound-MCP-AI · Install
Installation
- Clone this repository:
``bash git clone https://github.com/MorDavid/BloodHound-MCP-AI.git cd BloodHound-MCP-AI ``
- Install dependencies:
``bash pip install -r requirements.txt ``
- Configure the MCP Server in Claude Desktop:
```json { "mcpServers": { "BloodHound-MCP": { "command": "python", "args": [ "<Your_Path>\\BloodHound-MCP.py" ], "env": { "BLOODHOUND_URI": "bolt://localhost:7687", "BLOODHOUND_USERNAME": "neo4j", "BLOODHOUND_PASSWORD": "bloodhoundcommunityedition" } } } }