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Scan websites for agent-readiness and generate required AI integration artifacts.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "agent-ready-mcp" yet — see the docs or source repo.
Scan https://example.com for agent-readiness, evaluate whether the site is easy for LLMs and AI agents to consume, and generate improvement suggestions plus the required llms.txt, structured data, and WebMCP scaffold.
An agent-readiness assessment report plus ready-to-use llms.txt, structured data examples, and a WebMCP scaffold.
For our product documentation site at https://docs.example.com, generate the necessary files that make it easier for AI agents to understand and use, including llms.txt and a WebMCP scaffold, and identify gaps in the current information architecture.
A set of integration deliverables for the docs site, draft file contents, and information architecture recommendations.
Review the following websites for agent-readiness: https://a.example.com, https://b.example.com, and https://c.example.com; prioritize issues and generate recommended llms.txt, structured data plans, and WebMCP scaffolds for each.
A multi-site comparison report with prioritized issues and recommended generated artifacts for each site.
Scan a website and generate an agent readiness evaluation report.
Scan any URL for AI agent readability and manifest support.
Scan websites or MCP servers and get a signed trust-readiness scorecard.
Scan AI agents, MCP servers, and skills for security risks.
Test API compatibility for AI agents with scores, grades, and recommendations.
Scan MCP server configs for injections, secrets, and dangerous commands.