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Kindly Web Search MCP Server

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…

Install

$claude mcp add --transport stdio kindly-web-search `

Install command taken from the project's README or registry entry. Always review code before running it.

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%. 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!

How to use the suite

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.

Kindly Web Search MCP Server

Why do we need another web search MCP server?

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.

The Real Problem

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