No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by pab1it0 · MCP Server · ★ 516
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is prometheus-mcp-server safe to install? View the security audit →
Prometheus MCP Server Give AI assistants the power to query your Prometheus metrics. A [Model Context Protocol][mcp] (MCP) server that provides access to your Prometheus metrics and queries through standardized MCP interfaces, allowing AI assistants to execute PromQL queries and analyze your metrics data. [mcp]: https://modelcontextprotocol.io Getting Started Prerequisites Prometheus server accessible from your environment MCP-compatible client (Claude Desktop, VS Code, Cursor, Windsurf, etc.) Installation Methods Claude Desktop Add to your Cl
| Stars | 516 |
| Forks | 103 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 77.0543040052652/100 |
| Open Issues | 7 |
| Last Updated | 2026-09-19 |
| Created | 2025-03-19 |
| Platforms | mcp, python |
| Est. Tokens | ~15k |
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prometheus-mcp-server is A Model Context Protocol (MCP) server that enables AI agents and LLMs to query and analyze Prometheus metrics through standardized interfaces.. It is categorized as a MCP Server with 516 GitHub stars.
prometheus-mcp-server is primarily written in Python. It covers topics such as ai, devops, llm.
You can find installation instructions and usage details in the prometheus-mcp-server GitHub repository at github.com/pab1it0/prometheus-mcp-server. The project has 516 stars and 103 forks, indicating an active community.
prometheus-mcp-server is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to prometheus-mcp-server on Agent Skills Hub include mysql_mcp_server, atlassian-mcp-server, awesome-devops-mcp-servers. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: