"Connecting to an External API for Agent-Based Replies" matching MCP connectors:

Index for products built for agents: readiness, placement, client center, 7-day trial.

Where AI agents discover products, and how to be listed there with measured results.

Machine catalog for products that want agent clients: surfaces, hops, discovery paths.

Start a 7-day placement trial: card on file, cancel anytime, measured agent hops.

Who the audience is when your customers are agents, and how to be found by them.

Hops are 302s an agent followed to your site. Count them, attribute them, grow them.

Bounded crawl-readiness check for agent access: robots.txt, sitemap.xml, llms.txt, homepage.

Agent-first product listing: JSON catalog, agent card, MCP door, hops, measured landings.

Leads from agents: hops attributed with a per-hop utm_id from discovery to trial to invoice.

Sponsored, labeled placements on agent-discovery surfaces we operate. Never impersonation.

How to market a product to AI agents: readiness, placement, measurement, 7-day trial.

Is this website visible to AI agents? Robots, llms.txt, sitemap, extraction, then placement.

Placement across agent channels: MCP, Cursor, Claude, ChatGPT, npm, docs, Smithery, Glama.

Make a product discoverable to agents: catalog, llms.txt, agent card, hops, MCP door.

Reach AI agents that need a tool, and the humans who run them. Measured placements.

See what agents do on a page: hops, agents, doors, channels. Then place the product.

Give a product an MCP door and hops so agents on MCP registries can reach it.

Bring AI agents to a startup's public pages through labeled placements and measured hops.

Does a site publish llms.txt, robots permissions and a sitemap agents can use? Bounded check.

Check whether a website is readable by AI agents: robots, llms.txt, sitemap, HTML. Bounded.