Agent Fix Loop
Automate AEO improvements with an autonomous audit-fix-verify loop using the MCP tools.
Overview
The agent fix loop is a pattern where an AI agent audits a website, identifies weak criteria, applies fixes, and re-audits to verify improvement. This can run autonomously or with human-in-the-loop checkpoints.
The Loop
Repeat if score < target
- Audit - Submit domain or fetch existing audit
- Analyze - Get fix prompts sorted by score impact
- Fix - Apply changes to your website (schema, content, config)
- Re-audit - Request fresh audit after changes
- Compare - Check new score vs previous, identify remaining gaps
MCP Approach
With MCP tools, your AI agent can run the same loop interactively in Claude Code or Cursor. Instead of writing code, you describe what you want in natural language:
"Check the AEO audit for my-domain.com with fix prompts"
aeo_get_audit({ slug: "my-domain-com", include_fix_prompts: true })
"Apply the top 3 fixes to my codebase"
Agent reads fix prompts, modifies your source files
"Re-audit my-domain.com"
aeo_request_reaudit({ slug: "my-domain-com" })
"Check the audit status"
aeo_get_audit_status({ slug: "my-domain-com" })
"Show me the new score and remaining fixes"
aeo_get_audit({ slug: "my-domain-com", include_fix_prompts: true })
aeo_get_opportunities for a focused view of what to fix next - it returns only opportunities and fix items without the full scorecard and findings.Fix Prompt Format
Fix prompts are returned as part of the audit response when you include the fix_prompts parameter. Each prompt targets a specific criterion and includes actionable instructions:
potential_impact so you fix the highest-value items first.Best Practices
- Start with the top 3 highest-impact fixes
- Wait for re-audit to complete before making more changes (avoid stacking)
- Set a target score (e.g., 80) and loop until reached
- Use version comparison to track progress over time
- Combine with visibility reports for a complete picture