What Is MCP in AI, and How It Changes AI Integration

Before MCP, every AI application needed its own connector for every system it used. A company with ten AI tools and twenty internal systems could face up to two hundred separate integrations, each with its own authentication, testing and maintenance. MCP reduces that to one server per system and one client per AI application.

MCP standardizes how an AI model discovers, describes and calls external tools, which turns each integration from a one-off project into a reusable component that any compatible AI application can use.

This article explains what MCP is, how it works, what the July 2026 version of the specification changed and when it makes sense for a company to build an MCP server. It is not a coding tutorial, and it does not compare individual MCP server products or AI assistants.

Key takeaways

• MCP connects AI applications to tools and data through one standard, so each system needs one integration instead of one per AI tool.
• An MCP server exposes three kinds of capability: tools the model can call, resources it can read and prompts users can choose.
• The protocol is open and is maintained under the Agentic AI Foundation, part of the Linux Foundation.
• The 2026-07-28 specification made MCP stateless, which lets servers run on ordinary load-balanced web infrastructure.
• Security is the main design task, because every tool you expose is something a model can be manipulated into using.
• An MCP server is worth building when several AI applications need the same system or when customers want to use your product from their own AI assistants.

The problem MCP solves

AI assistants become useful at work when they can reach company systems: the CRM, the knowledge base, the ticketing tool and the data warehouse. Each connection used to be custom code written for one AI application and one system.

The pattern is familiar from other standards. Once a protocol is shared, the cost of connecting a new component falls, and teams can focus on the system itself.

How MCP works

The MCP architecture has three participants.

• The host is the AI application the user works in, such as a chat assistant, a code editor or an agent your team has built.
• The client is a component inside the host that maintains the connection to one MCP server. A host creates one client for each server it connects to.
• The server is a program that exposes the capabilities of a system, such as a CRM, a database or a document store. It can run locally on a user's machine or remotely as a web service.

An MCP server can offer three kinds of capability.

• Tools are actions the model can call, such as searching deals, creating a ticket or running a database query. Each tool has a name, a description and a schema for its inputs.
• Resources are data the application can read for context, such as a file, a record or a database schema.
• Prompts are reusable templates that users can pick, such as "summarize this account" or "draft a renewal email".

What happens when a user asks a question

1. A sales manager asks the assistant which open deals over $50,000 have had no activity this month.
2. The host already knows the tools offered by the connected CRM server and passes their descriptions to the model.
3. The model chooses the deal search tool and fills in its parameters: status open, value above $50,000, no activity in the last 30 days.
4. The client sends the call to the CRM server, which checks the user's permissions and queries the CRM.
5. The server returns the matching deals, and the model writes the answer, with an offer to draft follow-up emails.

The same CRM server works with any MCP-compatible assistant the company adopts later, without new integration work.

Who maintains MCP

Anthropic introduced the Model Context Protocol as an open standard in November 2024. Other major AI platforms adopted it over the following year, and in December 2025 Anthropic donated MCP to the newly formed Agentic AI Foundation, a fund under the Linux Foundation co-founded with Block and OpenAI. According to the MCP project's announcement, the protocol had more than 10,000 active servers at that point and client support in ChatGPT, Claude, Cursor, Gemini, Microsoft Copilot and Visual Studio Code.

Neutral governance matters for companies deciding whether to invest. A server built today is not tied to a single AI vendor's roadmap.

What the 2026-07-28 specification changed

The specification released on 28 July 2026 is the largest revision since the protocol launched. Its changes matter most to teams that host MCP servers at scale.

Stateless requests

The session handshake and the session ID header are gone. Each request now carries its own protocol version, client identity and capabilities, so any server instance behind a load balancer can answer any request. Servers can be hosted like any other web service, without shared session storage.

Routing headers and caching

HTTP requests now name the method and the tool in headers, so API gateways can route and authorize calls without reading the request body. List responses include caching hints, which reduces repeated discovery calls.

Authorization

Authorization is more closely aligned with OAuth and OpenID Connect. Clients must validate the issuer of an authorization code, and Dynamic Client Registration is deprecated in favor of Client ID Metadata Documents.

Extensions and deprecations

Long-running work moves to an official Tasks extension, and MCP Apps lets servers provide their own user interfaces. Sampling, Roots, Logging and the older HTTP with server-sent events transport are deprecated, with a formal policy that keeps deprecated features working for at least twelve months.

Teams that already run MCP servers should plan a migration within that window. Teams starting now can build on the stateless model from the beginning.

MCP, APIs and function calling

MCP does not replace your APIs. An MCP server usually wraps an existing API and describes it in a way that models understand.

For a single AI feature inside one application with two or three fixed actions, direct function calling is simpler. Our guide on adding AI to an existing app covers that pattern.

Security comes first

Every tool on an MCP server is an action a model can take on a user's behalf. Plan these five controls before the first server goes live.

• Prompt injection through content. A document, email or web page that the model reads can contain hidden instructions. The OWASP Top 10 for LLM applications lists prompt injection as the leading risk. Treat all retrieved content as untrusted, and never let it trigger sensitive actions on its own.
• Untrusted servers. A third-party server's tool descriptions go straight to the model, so a malicious server can steer its behavior. Install servers only from trusted sources, pin their versions and review updates.
• Over-broad permissions. A server that connects with an admin token gives every user admin power through the model. Use each user's own credentials through OAuth, and grant the smallest set of permissions each tool needs.
• Actions without confirmation. Tools that write, delete, send or pay should ask the user to confirm before they run.
• Missing audit trail. Log every tool call with the user, the parameters and the result, so that unusual behavior can be traced.

When should a company build an MCP server?

You sell a software product

Your customers increasingly work inside AI assistants. An MCP server lets them search, update and report on their data in your product from those assistants. Treat it like a public API, with versioning, authentication, rate limits and documentation.

You run several internal AI tools

One MCP server for each core system, such as the CRM, the knowledge base and the ticketing tool, can serve every internal assistant and agent. Access rules and logging live in one place for each system.

You are building an AI agent

An agent's tools become reusable and replaceable when they sit behind MCP. The agent can switch models or hosts without rebuilding its integrations. Our comparison of AI workflows and AI agents helps decide whether an agent is the right design in the first place.

When to wait

If one application uses a handful of fixed functions, or if the target system has no stable API yet, a direct integration is faster to build and easier to maintain. MCP can be added later, once more than one AI application needs the same access.