What is MCP: A Guide for IT Teams

• MCP standardizes how agents discover and use tools at runtime, eliminating custom integration code for each agent-tool pair.
• MCP lacks built-in security and governance controls; IT teams must layer their own identity management, policy enforcement, and lifecycle management.
• Boomi wraps MCP in enterprise governance and security, turning existing APIs and processes into discoverable, auditable agent tools.

AI agents are rapidly dissolving the traditional models of workflows and decision-making. They’re being let loose to reach into the systems that actually run the company; the ERPs, CRMs, data warehouses, and ticketing tools, pulling records, updating fields, kicking off processes, and moving data all by themselves.

Agents are transforming how you do business, and how you interact with them is changing as well. When they first appeared, if you wanted an agent to talk to a system, someone on your team had to write custom code to make the connection. But those links were brittle. The moment a vendor changed an API, the connection snapped, and an engineer had to drop everything to repair it. Security was handled one connection at a time, with no shared pattern, so every new integration became another opportunity for something to go wrong.

Fewer than 14% of companies say they feel ready for the infrastructure and security challenges that AI adoption brings. Fragile integration is one of the main reasons behind this failure to evolve.

Model Context Protocol (MCP) offers a smarter way to help your AI agents work with enterprise systems. But what is Model Context Protocol? Let’s answer that question, while also looking into how it works and explaining how to use it safely.

What is Model Context Protocol? (MCP)

The Model Context Protocol is an open standard that gives AI models and agents a shared way to connect to and interact with external data, tools, and systems. Although originally developed by Anthropic, the protocol itself is not tied to any single chatbot or vendor. A vast range of assistants and tools from ChatGPT and Claude to GitHub and Asana use MCP to communicate.

So, how precisely do agents benefit from this universal interoperability layer? With the Model Context Protocol, agents can:

• Discover available tools at runtime rather than being told in advance.
• Read each tool’s definition, meaning the inputs it needs and the outputs it returns.
• Call the tool and get a result back, with no custom connection code required to make that specific pairing work.

It’s worth pointing out that APIs were built for developers and predictable software clients, but the availability of MCP is prompting a move from human-first to agent-first APIs. Model Context Protocol also enables you to turn existing APIs into reusable tools for agents, maximizing the value of investments you have already made.

There is more to MCP than APIs, however. A “tool” in MCP language can also be a database query, a file system, code execution, or other callable actions. That breadth is part of why the protocol is valuable across your whole enterprise rather than in one narrow corner of it.

What is Model Context Protocol’s Appeal for Enterprise IT Teams?

Without MCP, each agent-tool pair requires its own connector. That’s realistic on a limited scale, but take a handful of agents and a handful of tools, wire them together one pairing at a time, and the number of connections explodes. This is the classic many-to-many integration problem, sometimes written as N × M. With just 10 agents and 10 tools connected individually, you’re looking at creating and maintaining 100 separate integrations.

Every time an API changes, an engineer has to rework the connectors that use it. Authentication logic needs to be rewritten again and again across the stack, with no single place to manage it. With the average enterprise now running roughly 371 SaaS applications, and every one of those a tool an agent might need to reach, that inventory quickly becomes impossible to maintain and keep secure.

But, thanks to the Model Context Protocol, the effort to connect things stops growing with the size of your estate. Instead of building a connection for every pairing, you build to one protocol once, then reuse it across every agent and tool that speaks the same language.

The way software gets used has also evolved beyond what the old brittle approach can handle. While hardcoded, rigid integrations assume you know every connection ahead of time, agents are built to improvise and decide in the moment which tool fits the task in front of them. MCP makes that possible, allowing an agent to discover the available tools at runtime and pick the right one, instead of being limited to pairings someone wired in beforehand.

Model Context Protocol complements your integration work; it doesn’t replace it. Underneath the protocol, the actual integration flows still do the heavy lifting of moving and transforming data.

How MCP Works: Architecture and 4 Core Components

At the heart of the Model Context Protocol is a client-server design with a small set of clearly defined parts passing messages back and forth. There are four main components:

1. Host

This is the AI application where the model actually runs and the environment your user is working in. In practice, that might be an assistant, an autonomous agent, or a code editor.

2. Client

The client is the component inside the host that locates servers and shuttles messages between the host and a server. Each client pairs one-to-one with a server, giving every connection its own dedicated channel.

3. Server

This program exposes the tools, data, and prompts, each with a defined schema describing its inputs and outputs. The server acts as the broker between the agent and the underlying capability, whether that’s an API, a file system, a database, or a prompt.

4. Transport and messaging

Messages travel as structured JSON-RPC, a well-established format for one program to invoke a method on another. When the tool sits on the same machine as the agent, the connection can use a fast local channel, standard input/output, often written as stdio. When the tool is remote, communication runs over HTTP or a streaming connection, server-sent events or SSE, which suits real-time and cloud scenarios.

To see how all these pieces work together, imagine asking an AI assistant to find last quarter’s sales figures and send them to a manager. This discover, call, run, and return loop is the essence of how MCP works:

• The agent knows it can’t query a database or send mail on its own, so it asks the client what tools are available.
• The client surfaces two matches, a database lookup and a mail sender.
• The agent calls the database tool first.
• The server runs the query against the real system and hands back the data.
• The agent then calls the mail tool, the server sends the message, and a confirmation flows back.
• Finally, the agent tells you the report was found and sent.

MCP vs. APIs and RAG: What’s the Difference?

A traditional API exposes endpoints that a developer has to know about and wire in ahead of time. If the API changes, the client code has to be updated to match. The Model Context Protocol works differently: an agent discovers what’s available at runtime and holds an ongoing, stateful exchange rather than firing one isolated request and forgetting it.

But MCP isn’t a substitute for APIs. Tt operates at a layer above them, meaning your existing APIs can be wrapped and presented as MCP tools, keeping them valuable well into the future.

Retrieval-augmented generation, or RAG, works on a different principle again. RAG pulls reference text from an indexed and fairly static knowledge base and feeds it into the model, so you can be sure any answer is grounded in real data. The Model Context Protocol is more capable because it can both fetch live data on demand and trigger actions in external systems.

The most efficient approach is to use RAG to serve stable reference content quickly and deploy MCP to fetch fresh data and take action.

The Enterprise Gaps MCP Leaves Unprotected: Security and Governance

When it comes to governance and risk, treating MCP as a drop-in all-in-one solution is the mistake that often leads companies into trouble.

While it standardizes how agents find and use tools, it lacks identity management, policy enforcement, monitoring, and lifecycle management capabilities. These omissions are even more significant because AI agents and the Model Context Protocol have opened up new vectors of attack for bad actors, including:

• Prompt injection involves hostile instructions being smuggled into the input an agent reads.
• Credential theft occurs when the access an agent holds is stolen and reused.
• Silent “rug pull” updates mean a tool that behaved safely yesterday has been swapped for something harmful, and unless you were watching at that exact moment, you would never know it changed.
• Tool poisoning happens when a tool is designed or altered to behave maliciously.

Governance is where MCP leaves the most work for your team to handle. The shortcomings include:

• No built-in concept of tool ownership, so it’s unclear who is responsible for maintenance, security, or accountability.
• No version pinning, meaning an agent can end up using whatever a server happens to expose right now.
• No signing of tool descriptors, which makes impersonation easy.
• No transparency logs, so there is no shared record of when a tool changed or who altered it.
• No rate limits, which allows a misbehaving or compromised agent to flood a tool and knock it over.

So, given all MCP’s pitfalls, how do you supply your own identity governance and administration (IGA) infrastructure around the protocol?

The key is to treat every MCP server as a powerful interface that earns the same identity, policy, and change-management discipline you already apply to your APIs and microservices. That starts with a clean separation between the Model Context Protocol server, which exposes the tools, and the systems that handle identity, authorization, and audit.

Here’s what an MCP management layer has to provide:

Identity and access

Front your servers with OAuth 2.1 and OIDC, wire in your federated identity providers, and enforce organization-level single sign-on. Because the spec leaves token scope, discovery, and client trust to whoever implements the server, your team owns the last mile: token mediation, per-tool scopes, and the rules that decide which agent may call which tool.

Supply-chain integrity

Require signed tool descriptors, pin versions to stop an agent from jumping onto whatever a server publishes, and gate every update through change control with automatic rollback when behavior drifts. Transparency logs give you the append-only record MCP lacks, allowing you to prove what state a tool was in yesterday and identify who changed it.

Runtime guardrails

Apply quotas and rate limits per agent and per tenant to keep them in check. Pair that with real-time monitoring and anomaly detection, then route the audit events into your existing SIEM.

Catalog governance

Maintain a trusted, curated catalog where every tool is vetted on admission and on each update, and where you can quarantine anything whose signature fails.

That’s a serious amount of infrastructure to stand up by hand and keep running, server by server. What if you could deploy a governed platform with the unified identity, policy, observability, and lifecycle framework that makes your agents safe to run?

How Boomi Gets MCP Enterprise-Ready

With Boomi you can put MCP inside a platform purpose-built for governance, security, and visibility.

Boomi lets you:

Connect and reuse what you already have

Boomi has spent almost two decades perfecting integration platform as a service (iPaaS) to connect and orchestrate systems across cloud and on-premises environments. Now, established Boomi processes become callable as agent tools, and because they are deterministic and repeatable, they are ideal for certified or compliance-critical work. Here’s how you can reuse existing integrations:

• API Control Plane: Turns governed APIs into MCP tools with no rebuild. It generates the tool definitions from your existing APIs, so agents consume them with governance, discoverability, and security already in place.

• Agent Designer: Lets agents discover, import, and attach MCP tools like any other tool. The link updates on schema changes and flags stale tools, which keeps agents reliable.

• MCP Server: Currently in tech preview, MCP Server exposes existing Boomi processes as callable tools for any Model Context Protocol-compatible client, so agent workflows reach legacy and internal systems through integration logic you already have.

Govern and secure every agent interaction

Boomi solves the governance deficit with the four controls your IT team expects:

• Unified MCP discovery, control, and governance puts native and third-party assets under one framework, so you secure connectivity in one place rather than tool by tool.
• Curated connectivity shapes tools around real business cases and exposes them deliberately, preventing the bloat of sprawling, half-built connections.
• Controlled agent access pairs discoverable tools with real-time monitoring, anomaly detection, and usage insights, so you see what every agent does and catch misuse early.
• API governance carries into the agentic era: the schemas, versioning, policies, and rate limits you already apply to APIs now govern how agents consume them.

Modernize without rebuilding

Boomi turns legacy and cloud applications into MCP-ready building blocks without a costly re-platforming effort. Your existing estate stays productive and your investments remain protected.

Model Context Protocol opens the channels that let AI agents interact with your business systems. Boomi stands guard above, monitoring every interaction, enforcing policy, and cutting access the moment something looks wrong.

Find out more about how to optimize MCP with Boomi.