How does MCP relate to AI agents?

An agent needs tools in order to act. MCP provides a standard way for an agent to discover and call those tools. Instead of each tool being connected with custom code, the agent connects to an MCP server that exposes the tools through the standard interface.

Is MCP specific to AWS?

No. MCP is an open standard and is not specific to any single provider. On AWS, an MCP server can be used to expose AWS services or Lambda functions to an agent, but the protocol itself is general.

Security depends on how a server is configured. Because a server grants access to tools and data, it should authenticate its clients, validate inputs, and grant only the permissions that are required, following the principle of least privilege.

MCP Explained for AWS Developers

Q: What is the Model Context Protocol?

The Model Context Protocol, known as MCP, is an open standard that defines how an AI application connects to external tools and data sources. A protocol is an agreed set of rules for communication. MCP allows the same tool to be used by different AI applications through a common interface.

Introduction

The Model Context Protocol, commonly written as MCP, is an open standard that defines how an AI application connects to external tools and data sources. A protocol is an agreed set of rules for communication between two programs. This guide explains what MCP is, why it exists, how it operates, and how it is used on AWS. The broader idea of an agent that uses tools is covered in the guide on AI agents.

What is it?

MCP defines a common interface between two parts. An MCP client is the part of the AI application that connects to servers and makes requests on behalf of the model. An MCP server exposes capabilities to the client. These capabilities include tools, which are actions the model can call, resources, which are data the model can read, and prompts, which are reusable instructions. Because the interface is standard, a tool that is exposed by one server can be used by any client that supports the protocol.

Why does it exist?

Before a standard existed, every connection between an AI application and an external tool was built with custom code. A tool that was integrated for one application could not easily be reused by another, and each integration had to be maintained separately. MCP was created so that tools and data sources are exposed once, through a common interface, and are then available to any AI application that speaks the protocol. This reduces duplicated effort and makes integrations reusable.

How it works

The AI application contains an MCP client. The client connects to one or more MCP servers and asks each server what it offers. Each server responds with the list of tools, resources, and prompts that it exposes. When the model decides that a tool is needed, the client sends the request to the appropriate server, the server performs the action, and the result is returned to the model. On AWS, an MCP server can be built to expose AWS services or Lambda functions, so that an agent can reach them through the standard interface.

Architecture diagram

AI application (contains an MCP client) | | standard requests v MCP server(s) --> expose tools, resources, and prompts | | | |-- a tool runs a Lambda function or calls a service | |-- a resource returns data the model can read v Result returned to the model through the client

Advantages

Standardization. One interface replaces many custom integrations.
Reusability. A tool exposed by a server can be used by any compatible client.
Decoupling. The AI application and the tools evolve independently.
Interoperability. Tools can be shared across different models and applications.

Disadvantages

Maturity. The standard is still developing, and practices continue to change.
Additional layer. A server introduces a component that must be operated and secured.
Security surface. A server that exposes tools and data must be protected carefully.

Common use cases

Connecting an agent to a set of internal tools through one interface.
Exposing company data to an AI application in a controlled way.
Making AWS services available to an agent through a server.
Sharing a common set of tools across several AI applications.

Best practices

A server should authenticate its clients so that only permitted applications may connect.
The inputs that a server receives should be validated before an action is performed.
A server should be granted only the permissions it requires, following the principle of least privilege.
The output of a tool should be treated with care and not trusted without checks.

Common mistakes

A server is granted broad permissions, which increases the risk if it is misused.
No authentication is applied, so any client can connect.
Inputs are not validated, which allows unsafe requests to reach a tool.
The result of a tool is trusted without being checked.

Amazon Bedrock provides the model that drives the AI application.
AWS Lambda can implement the tools that a server exposes.
AWS Identity and Access Management governs the permissions of the server and its tools.

Frequently Asked Questions

What is the Model Context Protocol?: MCP is an open standard that defines how an AI application connects to external tools and data sources. It allows the same tool to be used by different AI applications through a common interface.
What is the difference between an MCP client and an MCP server?: An MCP client is the part of the AI application that connects to servers and makes requests on behalf of the model. An MCP server exposes tools, data, and prompts to the client.
How does MCP relate to AI agents?: An agent needs tools in order to act. MCP provides a standard way for an agent to discover and call those tools, so that each tool does not require custom integration code.
Is MCP specific to AWS?: No. MCP is an open standard and is not specific to any single provider. On AWS, a server can expose AWS services or Lambda functions to an agent, but the protocol itself is general.
Is MCP secure?: Security depends on how a server is configured. Because a server grants access to tools and data, it should authenticate its clients, validate inputs, and grant only the permissions that are required.

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