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// AI DEBUGGING

Your AI can't fix what it can't see

You vibe-coded an app with Cursor or Claude. It works locally. Then production breaks. You paste the error into your AI — but it only sees what you pasted. Not your logs. Not the request that caused it. Not the 50 other errors that happened in the same minute.

The old way (broken)

1. App crashes in production
2. You see "500 Internal Server Error"
3. You open Cloudflare dashboard — "Error 1101, no logs"
4. You copy-paste the error into ChatGPT
5. ChatGPT guesses — "maybe it's a null reference?"
6. You change random code based on a guess
7. Deploy. Still broken. Repeat.

The FlareLog way (MCP)

1. App crashes in production
2. FlareLog's Tail Worker captures the full stack trace
3. You open Cursor (or Claude Desktop)
4. Ask: "What broke in production in the last hour?"
5. Cursor queries FlareLog via MCP — sees the actual error, stack trace, request URL
6. Cursor opens the file, highlights the bug, suggests a fix
7. Apply. Deploy. Fixed. Done.

Set up MCP in 2 minutes

For Cursor

Create .cursor/mcp.json in your project:

{
  "mcpServers": {
    "flarelog": {
      "url": "https://mcp.flarelog.dev",
      "headers": {
        "Authorization": "Bearer fl_your_api_key_here"
      }
    }
  }
}

For Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "flarelog": {
      "url": "https://mcp.flarelog.dev",
      "headers": {
        "Authorization": "Bearer fl_your_api_key_here"
      }
    }
  }
}

What you can ask your AI

  • ›"What errors happened in production in the last hour?"
  • ›"Show me the stack trace for the crash that happened at 3:42 AM"
  • ›"Which API endpoint is throwing the most 500s?"
  • ›"Find the request that caused this traceId and suggest a fix"
  • ›"Are there any patterns in the errors from the last deploy?"

FAQ

What is an MCP server?

MCP (Model Context Protocol) is a standard that lets AI tools like Cursor and Claude Desktop read external data sources. FlareLog's MCP server exposes your production logs, errors, and traces to your AI — so instead of copy-pasting error messages, your AI queries your dashboard directly.

Which AI tools support MCP?

Cursor (via .cursor/mcp.json), Claude Desktop (via claude_desktop_config.json), and any tool that implements the MCP client standard. ChatGPT does not yet support MCP natively — you'd use the FlareLog dashboard directly or copy-paste logs.

Is my production data safe with the MCP server?

Yes. The MCP server authenticates with your FlareLog API key (scoped per-project). Your AI can only read logs you've already ingested — it can't modify or delete anything. You can revoke access at any time by rotating your API key.

Can the AI actually fix bugs, or just find them?

Both. The AI reads the error, sees the stack trace, correlates it with recent deploys, and suggests a fix. In Cursor, it can open the file and apply the fix directly. In Claude Desktop, it gives you the code to paste. The quality depends on the AI model — Claude Sonnet and GPT-4 are excellent at this.

Let your AI see production

Free 10k logs/mo. MCP server included. No credit card.

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