Kindly Web Search MCP Server: Web search + robust content retrieval for AI coding tools (Claude Code, Codex, Cursor, GitHub Copilot, Gemini, etc.). Supports Serper, Tavily, and SearXNG.
# Add to your Claude Code skills
git clone https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-serverWeb search + robust content retrieval for AI coding tools.

Picture this: You're debugging a cryptic error in Google Cloud Batch with GPU instances. Your AI coding assistant searches the web and finds the perfect StackOverflow thread. Great, right? Not quite. Here's what most web search MCP servers give your AI:
{
"title": "GCP Cloud Batch fails with the GPU instance template",
"url": "https://stackoverflow.com/questions/76546453/...",
"snippet": "I am trying to run a GCP Cloud Batch job with K80 GPU. The job runs for ~30 min. and then fails..."
}
The question is there, but where are the answers? Where are the solutions that other developers tried? The workarounds? The "this worked for me" comments?
They're not there. Your AI now has to make a second call to scrape the page. Sometimes it does, sometimes it doesn't. And even when it does, most scrapers return either incomplete content or the entire webpage with navigation panels, ads, and other noise that wastes tokens and confuses the AI.
At Shelpuk AI Technology Consulting, we build custom AI products under a fixed-price model. Development efficiency isn't just nice to have - it's the foundation of our business. We've been using AI coding assistants since 2023 (GitHub Copilot, Cursor, Windsurf, Claude Code, Codex), and we noticed something frustrating:
When we developers face a complex bug, we don't just want to find a URL - we want to find the conversation. We want to see what others tried, what worked, what didn't, and why. We want the GitHub Issue with all the comments. We want the StackOverflow thread with upvoted answers and follow-up discussions. We want the arXiv paper content, not just its abstract.
Existing web search MCP servers are basically wrappers around search APIs. They're great at finding content, but terrible at delivering it in a way that's useful for AI coding assistants.
We built Kindly Web Search because we needed our AI assistants to work the way we work. When searching for solutions, Kindly:
✅ Integrates directly with APIs for StackExchange, GitHub Issues, arXiv, and Wikipedia - presenting content in LLM-optimized formats with proper structure
✅ Returns the full conversation in a single call: questions, answers, comments, reactions, and metadata
✅ Parses any webpage in real-time using a headless browser for cutting-edge issues that were literally posted yesterday
✅ Passes all useful content to the LLM immediately - no need for a second scraping call
✅ Supports multiple search providers (Serper and Tavily) with intelligent fallback
Now, when Claude Code or Codex searches for that GPU batch error, it gets the question and the answers. The code snippets. The "this fixed it for me" comments. Everyth...