What is an MCP server?
By Michael Olufuwa, founder of Tiadara
An MCP (Model Context Protocol) server is a standard way to connect an AI assistant like Claude directly to your tools and data, so it can read and act on them safely. Businesses use MCP servers to let AI work with their real systems instead of only what you paste into a chat.
What MCP does in plain terms
Without MCP, an AI assistant only knows what you type or paste into the conversation. That works for general questions, but it breaks down for business tasks because your data lives in systems like CRMs, databases, analytics platforms, and cloud storage.
An MCP server sits between the assistant and your system. It tells the assistant what data and actions are available, handles authentication, and returns the results in a format the assistant can reason about. The assistant gets context; your system keeps control.
Why businesses need it
Business AI that relies on pasted context is slow, inconsistent, and insecure. Someone has to export the data, clean it, feed it to the model, and then manually act on the answer. That is not a workflow — it is a demo.
MCP lets the assistant pull live data, run queries, and trigger actions in the systems you already use. The result is an AI teammate that works with your real tools instead of a chatbot that waits for you to do the typing.
MCP vs API, MCP vs RAG
An API integration moves data between systems on fixed rules. It is reliable but dumb — it cannot reason or adapt. MCP adds a reasoning layer: the assistant decides what to query, interprets the response, and can take the next step conversationally.
RAG (retrieval-augmented generation) gives an AI a long-term memory of documents. MCP gives it live hands and eyes. A support bot might use RAG to remember your help centre, and MCP to look up a customer record or create a ticket. They solve different problems and often work together.
Example: Meridian and Search Console
Meridian, our SEO product, connects Claude to Google Search Console through an MCP server. Instead of exporting CSVs and pasting traffic data into a chat, Claude can read Search Console directly, spot drops and opportunities, and explain them in plain language.
That is the difference MCP makes: the AI is not guessing from a paragraph you typed. It is looking at your actual data and reasoning from it.
Frequently asked questions
Is MCP secure?
MCP is a protocol, so security depends on the implementation. A well-built MCP server uses authentication, scopes permissions tightly, and logs what the AI does. It should expose only the specific actions and data the assistant needs, not an open connection to your systems.
Do I need an MCP server or an API integration?
Use an MCP server when you want an AI assistant to interact with a tool conversationally — reading data, taking actions, and reasoning across sources. Use a traditional API integration for fixed, predictable automation that does not need AI judgement. Many systems use both.
Can any AI assistant use an MCP server?
Only assistants that support the Model Context Protocol can use an MCP server. Claude, through Anthropic’s implementation, is the most prominent example today. Other assistants may adopt MCP over time, but the ecosystem is currently strongest around Claude.
If you want to connect Claude to your own tools and data, we design and build MCP servers that are secure, scoped, and production-ready.
See our AI agents and MCP server work →See Meridian in action →