MCP: how an AI agent accesses your data and your tools

Data and actions are scattered

Almost every process today is split across several tools. Each one knows only its own part. Take the web: Search Console knows search, the ad system knows paid traffic, analytics knows on-site behaviour and your CRM or e-shop knows the money. Each holds a piece of the truth. But none sees the whole path from the first impression to a paid order.

It's not just about data. The acting itself is split too. A single decision falls apart across several tools. In one you adjust the campaign budget. In another you create a task. In a third you rewrite a page. In a fourth you send a report. A person hops between them and carries context from one to the next by hand.

The value isn't in the individual tools, but in connecting them:One tool tells you something dropped. Why, you only learn by combining several sources. And doing something about it also works only across tools. This is exactly what's hardest to assemble by hand. Each tool speaks a little differently.

What MCP is

MCP (Model Context Protocol) is an open standard for connecting an AI agent to data sources and tools. One universal interface instead of a custom integration for every application. For a source (say Search Console) you write a so-called MCP server once, and it then serves any agent that speaks the standard. Our MCP server for Search Console is freely available on GitHub.

Not just for Claude

MCP is an open standard, not a feature of one assistant. Anthropic released it as open source. More and more tools support it: besides Claude, for example tools from OpenAI or editors like Cursor and VS Code. So you aren't locked to a single vendor.

A standard, not a dead end

You don't invest in a one-off integration, but in a standard. One MCP server serves many agents. And it survives even if you swap the agent. When a better model comes along, the connectors stay.

Almost anything can be connected

In principle anything with an API: analytics, ad systems, Search Console, CRM, databases, e-shop, payments. Many servers already exist. Your own can be added.

A connector isn't intelligence yet:MCP is just an interface for connecting. It exposes data and tools, nothing more. How many the agent links is a question of breadth. How good a conclusion it draws is a question of the AI itself: how smart it is and what it was trained on. These are two different layers. MCP handles the connection. The model handles the intelligence.

Two layers of MCP: read and act

MCP isn't only about accessing data. It also gives the agent the ability to act on it.

Read: data sources

The agent reads the data: rows from analytics, page content, a customer record, stock levels. It changes nothing, only looks. A safe but passive layer.

Act: tools and actions

The agent does something: creates a task, adjusts a campaign budget, edits a page, sends an email, changes a record. That has consequences in the real world. Greater reach, but also greater responsibility.

The jump is what matters. The agent isn't just a "smart report" that talks. It can both see and act across your systems.

The real breakthrough: several MCPs at once

"An AI has access to one tool" is not much. Almost every app does that today. It gets interesting when the agent holds several connectors at once and uses them together. It doesn't work with a single step, but with the whole process. And on both layers: it reads and it acts.

Read across sources → a complete picture

The agent puts data from several tools side by side and looks for links between them. It doesn't ask separately "what ads do" and separately "what analytics do". It asks what a campaign does to people's behaviour and ultimately to revenue.

Act across systems → orchestration

What the agent finds in one tool, it does right away in another. Read in analytics → adjust in ads → create a task. One smooth step instead of hopping between four apps.

The full loop: perceive → reason → act:The agent perceives across sources, weighs connections that aren't in any single tool, and acts across systems. The most valuable conclusions lie between the tools. That's exactly where hand-built reports miss them, because each looks only into its own column.

On the web: from impression to revenue, and back to action

Take the most common example: the web. We connect four sources to the agent via MCP and let it read them together.

Search Consolesees: impressions, positions, queries, clicks

Ad systemsees: spend, clicks, keywords, campaigns

Analytics (GA4)sees: on-site behaviour, paths, conversions

CRM / e-shopsees: orders, revenue, lead quality

The task: "Web revenue dropped last month, but traffic is holding. What happened?" The answer is scattered across all four tools. The agent reads them together and walks the whole chain in one question.

Analytics: traffic holds, but conversion dropped. The problem isn't how many people came, but what they did next.
+ Search Console: one page newly ranks for a broad, informational query. It brings people who don't want to buy, just to read.
+ Ad system: you're paying for a keyword the site already ranks first for organically. Money goes on visits that would come for free.
+ CRM: the most-clicked page looks great in Search Console, but its leads almost never close in the CRM.

A conclusion you won't see in any single tool:Revenue didn't drop because of traffic. Three things came together. A new informational query brings fewer buyers. Ads pay for a keyword you have for free organically. And the best-looking page sells worst. Each of these findings lies between two tools. An agent holding all four finds them in one question.

And it doesn't stop at findings. The agent uses the same connections to act right away: it pauses the ad keyword you have for free organically, creates a task to fix the weak page, adds a link toward purchase on the informational page, and sends the team a summary from all four sources.

An action with consequences needs a safeguard:Reading is safe. Acting has consequences. For irreversible or costly actions (pausing a campaign, sending an email, changing a live page) leave the final say to a human, or give the agent only narrow rights. More in the security section.

The reading side of this principle powers our AI SEO Consultant. It connects marketing and analytics data through MCP servers, including our open-source Search Console connector.

Across industries, not just marketing

The web is just an illustrative example. The same pattern holds for every process split across several tools, across industries:

Manufacturing

MES + machine data + production plan: why this batch is scrapping and how to reschedule the line, not just "production is down".

Maintenance

Machine sensors + repair history + spare-parts stock: catch a failure before it happens, and order the part right away.

Quality

Production measurements + complaints + suppliers: which material or supplier is behind a wave of defects, not just "scrap is rising".

Warehouse and logistics

Orders + stock + shipping: where exactly in the chain the delay arises, and adjust the order with the supplier.

Support

Tickets + product data + billing: why this segment complains, how much it costs us, and escalate or open a fix right away.

Finance

Accounting + sales + cash flow: which job is profitable only after counting all costs, and prepare a basis for the decision.

One foundation, tailored to fit:MCP is the technical foundation and this article describes how it works. How it's set up is always tailored to whoever uses it. A personal assistant for one employee gets a different scope of data and actions than a marketing consultant over web data. Same pipe, different connectors, different rights. Concrete services like AI SEO Consultant or a personal AI assistant are exactly that: a shared foundation tuned to the needs and rules of whoever uses it.

Before you connect: trust and security

Connecting data and tools means giving the agent access and rights. Reading is fairly safe. Actions already have consequences: the agent can permanently change, send or spend something. So be as careful as when granting access to anyone else.

Least necessary rights

Grant only the rights that are truly needed. Where reading is enough, don't grant write. Keep sensitive sources separate and keep track of where data goes.

A human for irreversible actions

For costly or irreversible steps, leave the final say to a human. MCP itself is no security guarantee. It's secure exactly as strictly as you set it up.

For a broader view of how to balance the benefit and risk of AI and how to set the permissions and reach of agents, see AI: value vs. security.

The second thing comes when the agent reaches for outside services, not just your own connectors. Then the question is whom to trust and how to verify what they return. That's covered by Trust in AI agents.

Summary

Data and actions today are split into separate tools. Each sees and can do only its own piece. MCP is an open standard. Through it an agent gains access to any source or tool with an API, without locking you to a single vendor. It gives it two layers: reading data and acting.

The breakthrough comes when the agent holds several connectors at once. It links data from different places, acts across systems and closes the whole loop: perceive → reason → act. The most valuable findings lie between the tools. That's exactly where hand-built reports can't reach. On the web you thus see the whole path from impression to revenue, and act right away. The same holds for manufacturing, maintenance, logistics and finance.

The point:Don't work with a single step of the process, but with the whole of it. And don't stop at looking. MCP brings the agent's data and tools together. The value is in connecting and simplifying. Instead of four reports and switching, one question and one decision over the whole picture.