BloodHound MCP, One Year Later: What I Learned About MCPs, Models, and Context

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TL:DR: The first version of BloodHound MCP proved that an LLM could converse with BloodHound. The current version drove home the lesson that MCP design is context design. The most useful changes were smaller but more flexible tools, better error recovery, domain-specific resources, and a prompt that guides the model instead of trying to teach everything up front.

Introduction: The First Version Worked, But Needed Improvement

When I released the first version of the BloodHound MCP, I was mostly thinking like a developer looking at an API surface. To me, more coverage meant more capability.

BloodHound has an extensive API and my goal was to expose as much of it to the MCP. That made sense for the first version. It proved the concept, and it gave users a way to ask natural-language questions against BloodHound data.

The feedback after release was useful. I got bug reports, pull requests, feature requests, privacy questions, and enough outside use to see the project differently. The first version worked, but it had inherited the shape of the API too directly.

That was my mistake.

An MCP tool is more than a function call. It is a chunk of context that is passed to the model with every query in a conversation

MCP Tools Are Not Free

The most obvious way to build an MCP server (or so I thought) is to map one API operation to one tool.

That is how the first version grew. If I had an endpoint for basic user information, that became a tool. If I had an endpoint for user admin rights, that became another tool. Group memberships? Another tool. Sessions? Another tool. RDP …

Vollständiger Originalartikel ↗

• Betroffenheit im eigenen Stack prüfen: Versionen/Komponenten abgleichen.
• Originalquelle / Hersteller-Advisory lesen: https://specterops.io/blog/2026/06/18/bloodhound-mcp-one-year-later-what-i-learned-about-mcps-models-and-context/
• Verfügbaren Patch oder Workaround einspielen und dokumentieren.