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Automation6 minUpdated 2026-07-06

How to use MCP servers with AI agents

The Model Context Protocol, or MCP, is a standard for how an AI client discovers and calls tools. Instead of every AI application inventing its own way to plug into your systems, MCP gives them a common interface: a server exposes a set of tools with clear descriptions, and any MCP-capable client can list them and call them. It is the layer that lets an agent reach out of its own conversation and do something real in your stack.

A hosted MCP server is that interface, run for you rather than something you stand up and maintain. This guide explains what MCP does in plain terms, what a hosted MCP server gives you, and how exposing your Ceven workflows and data through one lets any capable AI client use them as tools, with the same authorization and audit trail as everything else.

What MCP is, without the jargon

Think of MCP as a menu an AI client can read. The server publishes a list of tools, each with a name, a description, and the inputs it expects. The client reads the menu, decides which tool fits the task, calls it with the right inputs, and gets a result back. The value is standardization: the client does not need custom code for your specific systems, because the menu format is the same everywhere. That is the whole protocol in one idea.

What a hosted MCP server saves you

Running an MCP server yourself means hosting it, keeping it available, handling authentication, and maintaining the tool definitions as things change. A hosted MCP server, like the one Ceven exposes, takes that operational burden off you. You get the interface without the infrastructure, and the tools stay current with your workflows because they are hosted alongside them rather than in a separate service you have to keep in sync.

Exposing your workflows as tools

The point of Ceven's hosted MCP server is that your workflows and connected data become callable tools for AI clients. An agent in another application can call a Ceven workflow the same way it calls any other tool, hand it inputs, and get the result. This turns work you have already built into reusable capabilities that any capable client can invoke, rather than something locked inside one interface.

Authorization and audit still apply

Exposing tools over MCP does not mean loosening control. Calls run under the authorization you grant, and every call is recorded in the same audit trail as the rest of the platform, so you can see which client called which tool, with what inputs, and what it did. The standard interface is convenient; it is not a bypass around the controls that make automated action safe to trust.

When to reach for MCP

Use MCP when you want the work you have built to be usable from outside a single interface, by an AI client you or a partner runs elsewhere. If everything happens inside your own scheduled and triggered workflows, you may never need to think about it. But the moment you want another agent to be able to call your capabilities as tools, a hosted MCP server is the clean, standard way to let it, without building a bespoke integration each time.

Frequently asked

Do I need to run my own server?

No. Ceven exposes a hosted MCP server, so you get the standard tool interface without hosting or maintaining the infrastructure yourself. Your workflows and data are available as tools without extra operational work.

Is exposing tools over MCP safe?

Calls run under the authorization you grant and are recorded in the full audit trail. You control what is exposed, and you can see every call after the fact, so the standard interface does not weaken your controls.

What clients can call an MCP server?

Any MCP-capable AI client. The protocol is a standard specifically so that clients do not need custom code per system, which is what makes your hosted tools broadly reusable.

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