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, etc.). Supports Serper, Tavily, and SearXNG.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"kindly-web-search": {
"env": {
"GITHUB_TOKEN": "$GITHUB_TOKEN",
"SERPER_API_KEY": "$SERPER_API_KEY",
"TAVILY_API_KEY": "$TAVILY_API_KEY",
"SEARXNG_BASE_URL": "$SEARXNG_BASE_URL",
"KINDLY_BROWSER_EXECUTABLE_PATH": "$KINDLY_BROWSER_EXECUTABLE_PATH"
},
"args": [
"--from",
"git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server",
"kindly-web-search-mcp-server",
"start-mcp-server"
],
"command": "uvx",
"timeout": 120000
}
}
}Are you the author?
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Web search + robust content retrieval for AI coding tools.
This server supports HTTP transport. Be the first to test it — help the community know if it works.
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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%. Read more on Claude Code generation quality improvement.
Works with Claude Code, Codex, Antigravity, Cursor, Windsurf, and any agent that supports skills or MCP servers.
| Component | Role |
|---|---|
| tdd | Enforces TDD, requirements discipline, and peer review for every coding task |
| Serena | Semantic code navigation + persistent project memory |
| Kindly Web Search ← you are here | Up-to-date API and package documentation via web search |
| Lad MCP Server | Project-aware AI design and code review |
If you like what we're building, please ⭐ star this repo – it's a huge motivation for us to keep going!
1. Install three MCP servers and one skill:
2. Use the skill when requesting a feature:
Prompt your favorite AI coding agent (Claude Code, Codex, Cursor, etc.) as usual. Then just add Follow $tdd in the end.
> Build [your feature description]. Follow $tdd.

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 th