What is MCP (Model Context Protocol)?
A standard that lets AI tools (Cursor, Claude) read external data sources.
In plain English
MCP (Model Context Protocol) is a protocol that lets AI tools like Cursor and Claude Desktop connect to external data sources — like your production logs, your database, or your API. Without MCP, your AI can only see the code you have open in your editor. With MCP, your AI can query your FlareLog dashboard, read your production errors, and suggest fixes based on real data — not guesses. FlareLog runs an MCP server at mcp.flarelog.dev that exposes your logs, traces, and errors to any MCP-compatible AI tool.
Why it matters
This is the killer feature for vibe coders. Instead of copy-pasting error messages into ChatGPT and getting generic advice, your AI can see your actual production logs, search for the exact error, look at the stack trace, and suggest a fix that's specific to your codebase. It's like having an SRE pair-programming with you — except the SRE is Claude or GPT-4, and it never sleeps.
How FlareLog handles it
FlareLog's MCP server (mcp.flarelog.dev) exposes your logs, traces, errors, and cost data to Cursor and Claude Desktop. Setup takes 2 minutes — paste a URL and your API key into a config file. Your AI can then query your production data in natural language: 'What broke in the last hour?' or 'Show me the stack trace for this traceId'.
FAQ
Which AI tools support MCP?
Cursor (via .cursor/mcp.json) and Claude Desktop (via claude_desktop_config.json) are the main ones today. ChatGPT does not yet support MCP natively. Any tool that implements the MCP client standard can connect — the ecosystem is growing fast.
Is it safe to give my AI access to production logs?
Yes. The MCP server authenticates with your FlareLog API key, which is scoped per-project. Your AI can only READ logs — it can't modify or delete anything. You can revoke access at any time by rotating your API key. The data stays in your FlareLog dashboard; the AI just queries it.