Mcp_servers

MCP servers for each project.

1st project: k8s_cluster_1

2nd project: n8n_project_1

3rd project: bug_bounty_1

############### MCP & Vector DB ###############

For your specific project involving an AI agent on n8n that manages VMware infrastructure and Kubernetes deployment, I'll provide more targeted advice.

Let me search for information specifically about integrating MCP with infrastructure management and n8n.

Ran tool Based on the search results and your specific use case involving an AI agent app on n8n that manages VMware infrastructure and Kubernetes deployment, I can provide more targeted advice.

MCP Server Architecture for Your Infrastructure Management Project

Multiple MCP Servers Approach (Recommended)

For your complex infrastructure management use case, I recommend using multiple specialized MCP servers rather than a single server. Here's why:

1. Component-Specific Servers: Looking at the search results, there are dedicated MCP servers for:

   • Kubernetes (https://github.com/Flux159/mcp-server-kubernetes)
   • n8n workflows (https://glama.ai/mcp/servers/l1rov8ni7r)

2. Integration Benefits: Each server can focus on a specific part of your infrastructure:

   • One for VMware infrastructure management
   • One for Kubernetes deployment and management
   • One for n8n workflow orchestration

3. Maintenance Advantages: When you need to update or modify functionality for one component (e.g., Kubernetes), you only need to update that specific server without affecting others.

4. Security Isolation: Keeping credentials and sensitive data isolated to specific servers reduces the risk surface area.

Regarding Vector DB

Yes, you should include a vector database MCP server in your architecture for the following reasons:

1. Infrastructure Knowledge Base: A vector DB will allow your AI agent to store and retrieve information about your infrastructure (server configurations, deployment patterns, troubleshooting steps).

2. Available Options: There are several MCP servers for vector databases already available:

   • LanceDB MCP (https://github.com/RyanLisse/lancedb_mcp)
   • Lance MCP by adiom-data for RAG capabilities

3. Semantic Search: A vector DB enables your agent to perform semantic searches across your infrastructure documentation, logs, and configurations.

4. Memory Retention: Your agent will be able to store and retrieve previous infrastructure operations, which is crucial for maintaining state and learning from past deployments.

Recommended Architecture

I recommend setting up the following MCP servers for your project:

1. n8n MCP Server: To interact with your agent's workflows
2. VMware MCP Server: For VMware infrastructure management
3. Kubernetes MCP Server: For K8s deployment and management
4. Vector DB MCP Server (LanceDB or similar): For knowledge storage and retrieval
5. Shell/CLI MCP Server: For executing infrastructure commands

This modular approach will give you the flexibility to extend and maintain each component independently while allowing your AI agent to orchestrate across all of them.

Would you like more specific information about setting up any of these MCP servers for your infrastructure management use case?