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 AminForou · MCP Server · ★ 1.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp-gsc safe to install? View the security audit →
Google Search Console MCP server for SEOs March 2026 (v0.2.1): Data freshness, flexible row limits, multi-dimension filtering, reauthenticate tool, bug fixes, and multi-client support. See the Changelog for details. A Model Context Protocol (MCP) server that connects Google Search Console (GSC) to AI assistants, allowing you to analyze your SEO data through natural language conversations. Works with Claude, Cursor, Codex, Gemini CLI, Antigravity, and any other MCP-compatible client. This integration gives you access to property information, search analytics, URL inspection, and sitemap management—all through simple chat.
| Stars | 1,534 |
| Forks | 211 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 72.8529823447269/100 |
| Open Issues | 12 |
| Last Updated | 2026-09-15 |
| Created | 2025-03-14 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~19k |
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mcp-gsc is Google Search Console Insights with Claude AI for SEOs. It is categorized as a MCP Server with 1.5k GitHub stars.
mcp-gsc is primarily written in Python. It covers topics such as claude, google-search-console, mcp-server.
You can find installation instructions and usage details in the mcp-gsc GitHub repository at github.com/AminForou/mcp-gsc. The project has 1.5k stars and 211 forks, indicating an active community.
mcp-gsc is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp-gsc on Agent Skills Hub include Awesome-MCP-ZH, firecrawl-mcp-server, mcp. 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: