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Is Your Site Agent-Ready?

Scan your website to see if it's optimized for AI agents. We check five pillars — Discoverability, Content Accessibility, Bot Access Control, Protocol Discovery, and Commerce — across 19 signals.

We analyze your website across five essential categories to ensure complete autonomous agent readiness:

Discoverability

Scans for standard discovery markers such as robots.txt, sitemaps, and machine-readable headers.

Content Accessibility

Evaluates formatting compatibility, custom llms.txt files, and support for Markdown negotiation .

Bot Access Control

Checks agent-specific blocklists, Content Signals, and secure client validations like Web Bot Auth.

Protocol Discovery

Validates support for MCP Server Cards, custom Agent Skills, WebMCP, and secure OAuth Resources.

Commerce Integrations

Audits readiness for agent-to-agent transactions using standards like 402 payment requirements, Copilot monetization, and microformats like UCP or ACP.

You don't need a complex backend system to configure basic AI agent compatibility. Start with these three high-impact steps:

Step 01

robots.txt Rules

Allow user-agents like GPTBot or ClaudeBot, and explicitly specify your Sitemap URL.

Step 02

Create llms.txt

Publish a concise llms.txt Markdown guide in your root directory outlining your core routes, APIs, and site purpose.

Step 03

Discovery Headers

Add Link headers in HTTP responses pointing to your OpenAPI schema, MCP card, or API documentation path.

Dive deeper into the emerging specifications, frameworks, and developer standards for agent integration:

Cloudflare Agents Docs

Full reference guides for configuring schemas, deploying serverless MCP instances, and managing secure transaction interfaces.

Explore reference

Model Context Protocol

Anthropic's open-standard specifications to easily connect AI LLMs with local datasets, specialized APIs, and secure engines.

Explore reference

llms.txt Standard

The community specification proposal for structured Markdown files that offer dense, readable context to Large Language Models.

Explore reference