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 egebese · MCP Server · ★ 149
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is seo-research-mcp safe to install? View the security audit →
DataSEO MCP DataSEO MCP is a Model Context Protocol server for practical SEO research in AI assistants. It combines Ahrefs free SEO data, CAPTCHA-provider fallback, and optional OpenRouter-powered query planning behind a small set of MCP tools. [!CAUTION] This project is for educational and research purposes only. It interacts with third-party services including Ahrefs, CapSolver, Anti-Captcha, and OpenRouter. Users are responsible for complying with all applicable terms of service. Features DataSEO MCP is maintained by Ege Bese. For AI SEO and rank-tracking workfl
| Stars | 149 |
| Forks | 24 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 76.0063292886499/100 |
| Open Issues | 2 |
| Last Updated | 2026-01-07 |
| Created | 2026-01-07 |
| Platforms | mcp, python |
| Est. Tokens | ~35k |
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seo-research-mcp is A free SEO research tool using Model Context Protocol (MCP) powered by Ahrefs data. Get backlink analysis, keyword research, traffic estimation, and more — directly in your AI-powered IDE.. It is categorized as a MCP Server with 149 GitHub stars.
seo-research-mcp is primarily written in Python. It covers topics such as ahrefs, backlink-analysis, keyword-search.
You can find installation instructions and usage details in the seo-research-mcp GitHub repository at github.com/egebese/seo-research-mcp. The project has 149 stars and 24 forks, indicating an active community.
The top alternatives to seo-research-mcp on Agent Skills Hub include backlink_skills, Seo-Promt-Master, seobuild-onpage. 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: