Short answer: an MCP server is a small service that lets AI assistants (Claude, ChatGPT, Cursor or your own AI agents) use your company's systems and data through one open standard, the Model Context Protocol (MCP). Instead of building a separate integration for every AI tool, you build one MCP server for, say, your CRM, and every assistant that supports MCP can then look up customers, read deals or create tasks, with the permissions and audit logs you control.
Why MCP exists
AI assistants are only as useful as what they can reach. Without a standard, every AI tool needs its own custom connector to every system: five tools and ten systems means fifty integrations to build and secure. MCP replaces that with one interface on each side: AI applications speak MCP, and your systems are exposed through MCP servers.
Anthropic introduced MCP as an open standard in 2024. A growing range of AI applications now support it, including Claude, ChatGPT, Cursor and most agent frameworks.
How it works
There are three parts:
- The host: the AI application a person uses, such as Claude, ChatGPT or an internal assistant.
- The client: the part of that application that connects to MCP servers.
- The server: your code, which exposes your systems to the AI.
An MCP server offers three kinds of things:
- Tools: actions the AI can call, such as "search customers", "create invoice" or "check stock".
- Resources: data the AI can read, such as a document, a report or a database record.
- Prompts: ready-made instructions for common tasks, such as "summarise this account".
Servers run either locally (on a user's machine, often for developer tools) or remotely over HTTP, which is how most business systems are connected, with sign-in through OAuth.
What businesses use MCP servers for
- Sales: "Which deals stalled this week?" The assistant reads the CRM directly instead of someone exporting a spreadsheet.
- Operations: "What is today's stock of item X across both warehouses?" It queries the ERP.
- Support: replies drafted from real order and delivery data, not guesses.
- Finance: month-end questions answered from the accounting system, read-only.
- Engineering: AI coding tools that can read internal documentation, tickets and logs.
- Your own AI agents: one set of MCP servers becomes the toolbox for every agent you build. See agentic AI systems.
Security: where most of the work goes
Connecting AI to live systems is powerful, so the server has to be designed defensively:
- Sign-in and identity: users sign in (OAuth), and the AI acts as that user, never as an all-powerful service account.
- Least privilege: each tool exposes only what the role needs, and read-only access is the default.
- Human approval for changes: actions that send, delete, pay or update data ask a person first.
- Prompt-injection defences: content coming back from documents or emails is treated as data, not instructions, and risky tool chains are blocked.
- Audit logs: every call is recorded: who asked, which tool ran, what it returned.
- Rate limits and monitoring, like any production API.
Build, buy or wait?
Many popular SaaS products now publish their own MCP servers, so check before building. You need a custom server when the system is your own (an in-house ERP, a database, an internal API), when you need permissions that match your business rules, or when one assistant must combine several systems in a single answer.
How long and how much?
A focused server for one system is typically a 1–2 week project. Servers that span several systems with fine-grained permissions and approval flows take 3–4 weeks. Running costs are small: it is usually a lightweight API on your existing cloud. We price MCP projects as fixed milestones after a free scoping call.
How to start
- List the questions your team would ask an AI assistant if it could see your systems.
- Pick the one system that answers most of them, usually the CRM, ERP or main database.
- Decide which actions stay read-only and which need approval.
- Build one server, connect it to the assistant your team already uses, and measure the time saved.
Work with Nexariza AI
We build production MCP servers with OAuth, role-based permissions, audit logging and prompt-injection testing, and connect them to Claude, ChatGPT and your own agents. Read about MCP server development and AI integration, or get a free scope within 24 hours.
Frequently asked questions
What is MCP in simple words?
MCP (Model Context Protocol) is an open standard that lets AI assistants connect to outside systems in one consistent way. An MCP server exposes your data and actions, and any AI application that supports MCP can use them, within the permissions you set.
Is MCP only for Claude?
No. Anthropic introduced MCP as an open standard in 2024, and a growing range of AI applications now support it, including Claude, ChatGPT, Cursor and most AI agent frameworks. One well-built server can serve all of them.
How is an MCP server different from a normal API?
An MCP server usually sits on top of your existing APIs. It describes your tools and data in a format AI assistants understand, handles sign-in and permissions, and returns results the model can reason about, so you do not have to build a separate integration for every AI tool.
Is it safe to connect AI to our systems through MCP?
It can be, if the server is designed for it: OAuth sign-in, the least privilege each user needs, read-only access by default, human approval for actions that change data, audit logs, and testing against prompt injection. Security is most of the engineering work.
How long does it take to build an MCP server?
A focused server for one system, such as a CRM or a database, typically takes one to two weeks. Servers that span several systems with fine-grained permissions take three to four weeks. A free scoping call gives you a fixed quote.