Website Data API for AI Agents: Using MCP Tools for Checks

An AI agent can use a website data API when the vendor exposes its checks as MCP tools. The agent receives a tool name, a description and an input schema, then calls the check itself when a task needs facts about a domain. With BusinessMCP, each website check is both a REST route and an MCP tool at the same price.

Why agents want per-domain facts

Agents are good at reasoning and bad at knowing the current state of the internet. Ask one whether a prospect’s domain can send mail safely, or whether a client’s site blocks AI crawlers, and it will either guess or browse slowly. A structured check gives it a grounded answer in one call.

Typical agent tasks that benefit:

• Reviewing a list of client domains and producing a fix list.
• Pre-flighting a sending domain before a campaign.
• Answering a colleague’s question about whether GPTBot can reach a site.
• Triage during an incident: did a robots.txt change break crawler access?

The BusinessMCP website checks API was designed for this overlap. Its page says each endpoint has a REST route and an MCP tool with the same name, input and price, so what a script can do, an agent can do.

The four tools

MCP tool	Question it answers	Price per call
ai_readiness_grade	How readable is this site to AI agents and answer engines (0 to 100)?	$0.03
ai_crawler_access	Which AI crawlers does robots.txt allow?	$0.01
webmcp_check	Does the site expose in-page tools to browser agents?	$0.01
email_auth_check	Are MX, SPF, DMARC and DKIM set up, and what should be fixed?	$0.01

The input is a single required domain string. That simplicity is a feature for agents: there is little room to call the tool wrongly.

Connecting an agent

The vendor documents one MCP endpoint at https://businessmcp.com/api/hub/mcp, authenticated with the same bearer key as REST, per the data API overview. The exact client configuration depends on the agent product you use; BusinessMCP publishes setup notes in its docs, including a MCP config generator for common clients. Follow those rather than a generic recipe, because client formats differ and change.

Once connected, the agent lists the tools automatically. You can then prompt in plain language: “Check whether example.com blocks AI crawlers and has a DMARC policy.” A well-behaved agent should call two tools and summarise.

Key takeaway: an MCP tool is just an API call with a self-describing wrapper. The same cost, limits and failure modes apply, so govern it like any other metered API.

Guardrails worth setting

Agents can loop, retry and over-call. Per-call billing makes that a budget issue, not just a nuisance.

1. Dedicated key. Create a key used only by the agent so you can revoke it without touching scripts.
2. Small prepaid balance. Because the service is prepaid, the balance is a natural ceiling. Keep it modest.
3. Call caps in the prompt or harness. Tell the agent how many domains it may check per task, and enforce it in code if your harness allows.
4. Read-only intent. These checks read public data. Keep that separate from any tool that writes or sends.
5. Log tool calls. You want a record of which domains were checked and why.

At the published rates, a runaway agent calling all four checks on 1,000 domains would spend about $60, so the downside is bounded but real. See the pricing arithmetic for scenarios.

What agents are still bad at here

Do not let fluent summaries hide the checks’ limits.

• Unknown is not pass. The vendor reports an unreadable robots.txt as unknown, not allowed. Instruct the agent to report unknown explicitly.
• Readability is not citation. The AI readiness grader’s own page says it measures whether AI engines can read your site, not whether they cite you. An agent that says “your site will rank in AI answers” is overreaching.
• Served HTML only. The readiness check runs no JavaScript, so script-rendered content can be missed.

A short system instruction helps: “Report each check’s verdict and any unknown or error state verbatim. Do not infer outcomes the check does not measure.”

Agent or script?

Situation	Better fit
Same audit on 200 domains every week	Script
Ad hoc question from a colleague	Agent
Part of a larger research task	Agent
Alert on change in CI	Script

Scripts are cheaper to reason about and easier to test; agents are better when the next step depends on what the check finds. Many teams use a script to collect data and an agent to explain it. If you go the script route, start from the Python example. For what the grade itself means, read the AI readiness score explained.

Vendor features and prices change, so confirm tool names, limits and pricing in the current documentation before you build on them.

Example prompts and what good output looks like

Prompts that name the check, the domain and the expected form of the answer produce the most reliable behaviour.

Prompt	Tool calls expected	Good answer includes
“Does example.com let GPTBot and ClaudeBot crawl it?”	ai_crawler_access	Per-crawler verdict, and unknown if robots.txt was unreadable
“Can example.com send mail safely?”	email_auth_check	MX, SPF, DMARC, DKIM status and fixes
“Audit these 5 domains and rank the worst”	up to 4 per domain	A table, call count and any unknowns
“Is example.com agent-ready?”	ai_readiness_grade, webmcp_check	Score, plus the caveat that readiness is not citation

Notice that the last row involves two tools and a caveat. A good agent response states what was checked and what was not. If you see an answer that claims more than the tools measure, tighten the instructions.

Audit trail and review

Agents work best when a person can verify what happened. Ask the agent to include the raw verdict for each check next to its summary, and keep the tool call log. When an answer surprises you, the log shows whether the agent misread a result or the check genuinely reported it. For anything that leads to an action with consequences, such as changing DNS, have a person approve it. These checks only read public data, but the changes that follow from them may not be harmless.

Choosing between direct MCP and a wrapper

You can connect an agent to the vendor’s MCP endpoint directly, or you can write your own small MCP server that wraps the REST API and adds your rules: a call cap, a domain allow-list, caching. The wrapper costs more to build but gives you control over spend and behaviour. For a small team, direct connection with a modest prepaid balance is usually enough; for an organisation, the wrapper is often worth it. BusinessMCP also publishes material on building MCP servers if you go that way.

Finally, remember the agent is a consumer of the same service as your scripts. The unknown states, the 402 behaviour and the cost per call are identical, so the lessons in handling errors and unknown results apply unchanged.