The Model Context Protocol (MCP) is an open standard that lets an AI application connect to external tools and data through one common interface. Instead of writing a custom integration for every pairing of model and system, you expose a system once as an MCP server and any MCP-capable client can use it. Anthropic introduced it, and in December 2025 it was donated to the Linux Foundation, where it sits under the Agentic AI Foundation alongside other agent projects. Claude, ChatGPT, Gemini, Cursor, VS Code and Microsoft Copilot are among the clients that support it.
What MCP actually standardises
An MCP server describes three kinds of things to a client: tools the model can call (create a ticket, look up an order), resources it can read (documents, records), and prompts (reusable templates). The client discovers these at connection time, so the model learns what it can do without you hard-coding every function into each agent. The practical gain is less glue code and fewer one-off connectors to maintain.
When it is worth using
- Several agents or clients need the same system. One MCP server for your CRM or billing data beats three separate integrations.
- You want customers or partners to connect their own AI tools to your product. An MCP server is a clean way to offer that, and it is increasingly something B2B buyers ask about.
- You expect to swap or add models. The tool layer stays the same when the model changes. See which model should power your agents.
When it is not
If you have one agent calling two or three internal functions, a plain function-calling setup is simpler and easier to test. MCP adds a server to run, authenticate and monitor. Adopt it when reuse justifies that overhead, not because it is fashionable.
Design the tools, not just the wrapper
The most common mistake is to mirror an existing REST API one-to-one. Models work better with a small number of well-named tools that match tasks (find a customer, summarise an account) than with dozens of low-level endpoints. Write clear descriptions, return concise structured results, and make errors informative so the agent can recover.
Security comes first
An MCP server gives a model the ability to act. Treat it like any externally reachable API, and then go further:
- Authenticate every client and scope access to the least privilege needed. Prefer read-only tools until the agent has earned more.
- Require human approval for destructive or high-value actions.
- Assume tool output and retrieved content can contain hostile instructions. See our guide to AI agent security and prompt injection.
- Log every call with who, what and why, so you can audit behaviour.
- Only connect to MCP servers you trust. A third-party server is code that your agent will believe.
How we approach it
When an agent needs to reach several systems, we decide per project whether MCP, direct function calling or a framework such as LangGraph fits best (see LangGraph vs a custom agent loop). Whatever the choice, the build includes an evaluation suite and permissions defined up front. This is part of our fixed-price AI agent development and LLM integration work.
Frequently asked questions
What is the Model Context Protocol in simple terms?
MCP is an open standard that lets AI applications connect to tools and data sources through one common interface, so a system can be exposed once and used by many AI clients.
Is MCP only for Claude?
No. It was introduced by Anthropic but is now an open standard under the Linux Foundation and is supported by many clients, including ChatGPT, Gemini, Cursor, VS Code and Microsoft Copilot.
Should my SaaS product have an MCP server?
If customers or partners want to use your product from their own AI tools, or several internal agents need the same data, probably yes. For a single agent with a few functions, plain function calling is usually simpler.
Is MCP secure?
The protocol does not make an agent safe by itself. Security depends on authentication, least-privilege scopes, approval for risky actions, logging and defences against prompt injection in tool output.
Related: LLM integration · AI agents explained
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