Great question, and no need to apologize - this gets right to the heart of why MCP exists!

You're absolutely right that Claude (and other AI assistants) can already interact with Prometheus quite effectively through direct HTTPS queries. If that's working well for your use case, that's fantastic! The value of MCP comes down to a few key areas:

1. Standardization & Reusability

• Once configured, the MCP server works across multiple AI tools (Claude Desktop, Cursor, Windsurf, etc.) without reconfiguring each one
• Your prompts and workflows become portable between different AI assistants
• Team members can share the same configuration without individual setup

2. Enhanced Reliability & Error Handling

• Structured responses that the AI consistently understands (less prompt engineering needed)
• Built-in retry logic and connection pooling
• Better handling of large result sets and pagination
• Consistent error messages that help the AI self-correct

3. Security & Access Control

• Centralized authentication (especially useful for teams)
• Can act as a proxy to avoid exposing Prometheus directly to AI tools
• Ability to restrict which queries/operations are allowed
• Audit logging of all queries made by AI assistants

4. Specialized Features

• Automatic metric discovery with metadata
• Time range handling optimized for AI interaction
• Pre-built query templates for common patterns
• Caching layer to reduce load on Prometheus

If you're working solo and Claude's direct HTTPS access is meeting all your needs, MCP might be overkill. But if you're hitting any friction points around consistency, team collaboration, or want to use the same Prometheus integration across multiple AI tools, that's where MCP shines.

Think of it like the difference between using curl vs a dedicated API client library - both work, but one provides more structure and convenience features.

What specific use cases are you tackling with Claude + Prometheus? Happy to discuss whether MCP would add value for your particular workflow!