<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Claude-Integration on René Zander | AI Automation Consultant</title><link>https://renezander.com/tags/claude-integration/</link><description>Recent content in Claude-Integration on René Zander | AI Automation Consultant</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 02 Apr 2026 09:00:00 +0200</lastBuildDate><atom:link href="https://renezander.com/tags/claude-integration/index.xml" rel="self" type="application/rss+xml"/><item><title>MCP vs Custom API Integration: When Each One Actually Wins</title><link>https://renezander.com/guides/mcp-vs-custom-api-integration/</link><pubDate>Thu, 02 Apr 2026 09:00:00 +0200</pubDate><guid>https://renezander.com/guides/mcp-vs-custom-api-integration/</guid><description>&lt;p&gt;Every team I talk to that has shipped one Claude integration asks the same question within a month: should this tool be an MCP server, or should it stay as a tool definition inside our app? The answer gets framed as a technology debate, but it&amp;rsquo;s really a question about how many places you plan to use the same capability.&lt;/p&gt;
&lt;p&gt;Here is the short version. For about 90% of teams, a custom API integration written directly into your Claude code is the right call. The Model Context Protocol is the right call when you need the same tool surface across multiple LLM clients, when you are building a reusable internal platform, or when you are shipping tools for other people to hit from their own assistants. The rest of this guide walks through why, with the cost model and a decision tree at the end.&lt;/p&gt;</description></item></channel></rss>