I Built an MCP Server That Lets AI Autonomously Debug Salesforce - Here's How

I built sf-log-mcp, an open-source MCP server that gives AI assistants (Claude, Copilot, Cursor) the ability to autonomously fetch, analyze, and manage Salesforce debug logs. It detects "silent failures" that Salesforce marks as "Success" but are actually broken. Published on npm, 9 tools, 7 parsers, 101 tests. GitHub: github.com/Likhit-Kumar/SF-Logs npm: npx sf-log-mcp The Problem Nobody Talks About If you've ever debugged a Salesforce integration, you know the drill: Open Setup > Debug Logs Stare at a wall of logs showing Status = "Success" Manually download each .log file Ctrl+F through 50,000 lines looking for what went wrong Find out the "successful" callout actually returned {"error":"rate_limit_exceeded"} inside an HTTP 200 The Status field lies. In my experience, over 90% of real production issues are silent failures the code didn't crash, Apex didn't throw an unhandled exception, but the right thing didn't happen. Here's what "Success" actually hides: HTTP 200 with {"error"