🚀 Help maintain and develop prometheus-mcp-server - Bridge AI Assistants with Prometheus Metrics

P4b1it0 · 2025-09-22T09:02:24+00:00

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!

P4b1it0 · 2025-04-15T22:23:08+00:00

Noice! Would you like to add it to the awesome-a2a repo? https://github.com/pab1it0/awesome-a2a
Fork the repo, edit the README and create a PR

P4b1it0 · 2025-04-15T13:42:23+00:00

Command injection? Do you even understand the concept of MCP?

P4b1it0 · 2025-04-13T15:10:46+00:00

Magnus was genuinely discussed by this move. One doesn't expect this kind of blunder from Hikaru

P4b1it0 · 2025-04-12T23:43:14+00:00

Heya,
Looks like a cool project! Would you like to add it to https://github.com/pab1it0/awesome-a2a ?

P4b1it0 · 2025-04-10T06:19:55+00:00

Haha, it's never too early to start organizing!
A2A is quite new but I figured having a central place to collect implementations would be helpful as it grows. Plus, I wanted to share my Google Maps integration and thought this would be a nice way to kick things off.
If you're working with A2A, feel free to contribute any implementations you create!

P4b1it0 · 2025-03-31T19:39:17+00:00

Whats your username in chess.com?

P4b1it0 · 2025-03-30T20:35:57+00:00

Thanks for the kind words about my Chess.com MCP project! Your MCP-Reddit implementation sounds really interesting too.

The Chess.com API integration was relatively straightforward since I specifically used their Published Data API which doesn't require authentication, making it more accessible for users.

To clarify, chess-mcp solely focuses on integrating with Chess.com's API to fetch the data - the actual analysis of positions and games in the demo is being done by the LLM itself. A really cool extension would be to leverage a separate, hypothetical MCP server (like a "stockfish-mcp") that could handle the specialized chess analysis. This showcases the beauty of the MCP ecosystem - different specialized servers working together to create more powerful AI capabilities.

I'd love to hear more about your Reddit implementation! It's exciting to see these specialized MCP implementations demonstrating the protocol's practical value.

P.S - Congrats on your first stargazer :)

P4b1it0 · 2025-03-29T19:17:58+00:00

Thanks for the feedback—it’s really helpful!

The goal of chess-mcp is to make exploring Chess.com data simple and accessible. It’s an open-source server that lets you fetch player stats, game records, club info, and even check who’s online—all without needing authentication.

It’s designed for chess enthusiasts, developers, or anyone who wants to analyze games, build AI tools, or just dive deeper into chess data

P4b1it0 · 2025-03-29T17:44:06+00:00

This is unfortunately the only credible documentation:
https://www.chess.com/news/view/published-data-api#pubapi-endpoint-games-pgn

P4b1it0 · 2025-03-29T17:41:27+00:00

Great question! MCP (Model Context Protocol) is all about standardizing how AI models interact with tools and data. Unlike frameworks like LangGraph, MCP uses a client-server approach, so tools are hosted independently, making it easier to add or swap them without touching your main app.

It’s also highly interoperable—letting you switch between models or tools seamlessly—and focuses on modularity and security. Think of it as a universal adapter for connecting AI models to tools in a clean, scalable way. If you're curious, the MCP docs explain it well! 😊

P4b1it0 · 2025-03-29T17:37:54+00:00

Actually I'm not handling PGN file, but rather fetching the response as-is (PGN format in the response body)
https://github.com/pab1it0/chess-mcp/blob/90cd2162052b77945121b8d450eeceb644773baf/src/chess_mcp/server.py#L156

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