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 grafana · MCP Server · ★ 168
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is loki-mcp safe to install? View the security audit →
Loki MCP Server A Go-based server implementation for the Model Context Protocol (MCP) with Grafana Loki integration. Getting Started Prerequisites Go 1.16 or higher Building and Running Build and run the server: Or run directly with Go: The server communicates using stdin/stdout and SSE following the Model Context Protocol (MCP). This makes it suitable for use with Claude Desktop and other MCP-compatible clients. Project Structure MCP Server The Loki MCP Server implements the Model Context Protocol (MCP) and provides the following tools: Loki Query Tool The tool allows you to query Grafana Loki log data: Required parameters: : LogQL query string Optional parameters: : The Loki server URL (default: from LOKIURL environment variable or http://localhost:3100) : Start time for the query (default: 1h
| Stars | 168 |
| Forks | 37 |
| Language | Go |
| Category | MCP Server |
| Quality Score | 74.9004007693485/100 |
| Open Issues | 28 |
| Last Updated | 2026-09-04 |
| Created | 2025-04-10 |
| Platforms | go, mcp |
| Est. Tokens | ~18k |
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loki-mcp is An MCP ( Model Context Protocol ) Server for Grafana Loki. It is categorized as a MCP Server with 168 GitHub stars.
loki-mcp is primarily written in Go. It covers topics such as agentic-ai, agentic-workflow, golang.
You can find installation instructions and usage details in the loki-mcp GitHub repository at github.com/grafana/loki-mcp. The project has 168 stars and 37 forks, indicating an active community.
The top alternatives to loki-mcp on Agent Skills Hub include concierge, vibe-check-mcp-server, AgenticGoKit. 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: