The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
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.
README excerpt
Read full README on GitHub →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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