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by Shelpuk-AI-Technology-Consulting · MCP Server · ★ 385
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🔒 Is kindly-web-search-mcp-server safe to install? View the security audit →
Improve AI-generated code quality by 20% Web search + robust content retrieval for AI coding tools. Kindly Web Search is a part of the Shelpuk AI Technology Consulting agentic suite – a set of tools that together improve the code quality produced by AI coding agents by 15–20%. Works with Claude Code, Codex, Antigravity, Cursor, Windsurf, and any agent that supports skills or MCP servers. If you like what we're building, please ⭐ star this repo – it's a huge motivation for us to keep going! [](https://star-history.co
| Stars | 385 |
| Forks | 32 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.0541052255183/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-11 |
| Created | 2026-01-02 |
| Platforms | browser, claude-code, codex, gemini, mcp, python |
| Est. Tokens | ~20k |
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kindly-web-search-mcp-server is Kindly Web Search MCP Server: Web search + robust content retrieval for AI coding tools (Claude Code, Codex, Cursor, GitHub Copilot, Gemini, etc.) and AI agents (Claude Desktop, OpenClaw, Hermes, etc.. It is categorized as a MCP Server with 385 GitHub stars.
kindly-web-search-mcp-server is primarily written in Python. It covers topics such as ai, ai-agents, ai-tools.
You can find installation instructions and usage details in the kindly-web-search-mcp-server GitHub repository at github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server. The project has 385 stars and 32 forks, indicating an active community.
kindly-web-search-mcp-server is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to kindly-web-search-mcp-server on Agent Skills Hub include open-skills, autocontext, awesome-ai-coding-techniques. 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: