A detailed report on how AI assistants perceive the target URL, including crawler permissions, raw text content, structured data, and agent surfaces, along with specific fixes to improve visibility.
The agent uses the audit_ai_readiness tool to fetch the target URL and analyze its AI legibility. It checks which AI crawlers are blocked via robots.txt, counts words in raw HTML (excluding JavaScript-rendered content), parses JSON-LD structured data, and verifies the presence of agent surfaces like /llms.txt. The agent reviews the returned score and individual checks to identify gaps in AI visibility.
- Category: Research and knowledge
- Effect: Read-only
- MCP servers: agentbuilt MCP (https://koskamo.com/explore/agentbuilt-mcp)
- Source: agentbuilt MCP server & A2A agent — free AI-readiness audit for any URL (https://agentbuilt.dev/mcp)
- Source quote: "Fetches a URL and reports how legible it is to AI assistants and agents. Returns a score, every individual check, and the fixes."
- Playbook page: https://koskamo.com/explore/use-cases/audit-ai-readiness-and-crawler-access
Prompt: Audit the AI readiness of https://example.com. Run the audit_ai_readiness tool and provide a summary of the score, which crawlers are blocked, the amount of raw text available, and any missing agent surfaces or structured data.