Prometheus MCP Server

Created By
CaesarYangsa year ago
A Model Context Protocol (MCP) server for retrieving data from Prometheus databases.
Overview

What is Prometheus MCP Server?

Prometheus MCP Server is a Model Context Protocol (MCP) server designed for retrieving and analyzing data from Prometheus databases, enabling Large Language Models (LLMs) to perform various data-related tasks.

How to use Prometheus MCP Server?

To use the Prometheus MCP Server, set up a Python virtual environment, install the required packages, and run the server using the provided commands. You can also integrate it with the Claude Desktop app for easier access.

Key features of Prometheus MCP Server?

  • Data Retrieval: Fetch specific metrics or ranges of data from Prometheus.
  • Metric Analysis: Perform statistical analysis on retrieved metrics.
  • Usage Search: Explore metric usage patterns.
  • Complex Querying: Execute advanced PromQL queries for in-depth data exploration.

Use cases of Prometheus MCP Server?

  1. Retrieving and analyzing performance metrics from applications.
  2. Executing complex queries to gain insights from large datasets.
  3. Integrating with AI models for enhanced data processing capabilities.

FAQ from Prometheus MCP Server?

  • What is the purpose of the MCP server?

The MCP server allows for efficient data retrieval and analysis from Prometheus databases, facilitating advanced data operations.

  • How do I install the server?

You can install it via Smithery or manually by setting up a Python virtual environment and installing the required packages.

  • Can I contribute to the project?

Yes! Contributions are welcome, and you can follow the guidelines provided in the repository.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
CaesarYangs
Star
-
Language
-
License
-

Recommend Servers

View All
Bring your real authenticated browser session to AI coding agents. Local-first MCP server + Chrome MV3 extension. No cloud. No telemetry.
@Cubenest

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)

a day ago