Integrating MCP Servers with Amazon Bedrock AgentCore

Overview

Model Context Protocol (MCP) is an open standard that allows AI applications to integrate with other applications and data sources via a common interface.

An MCP server serves as an intermediary between an AI agent and corporate systems. Rather than creating a separate integration layer for each app, the developer builds capabilities on top of the common MCP endpoints.
In general, an MCP server offers three classes of functionality:

• Tools – executable actions, such as ticket creation, email sending, or customer data retrieval.
• Resources – structured documents, files, or datasets that add to the context of the agent.
• Prompts – templated prompts that facilitate common workflows.

Imagine MCP as the universal connector that helps different AI applications integrate with enterprise systems without custom implementation each time.

The key value of the MCP service lies in its interoperability. Many different AI frameworks, coding assistants, and enterprise applications can consume the same standardized set of tools.

What Is Amazon Bedrock AgentCore?

While MCP focuses on connecting tools, Amazon Bedrock AgentCore focuses on running AI agents in production.

AgentCore is an AWS-managed platform for easy deployment, scaling, governance, and observability of enterprise agents.

Unlike MCP, AgentCore does not replace it; rather, it delivers the infrastructure required to operate scalable intelligent agents.

These are some of its features:

• Secure agent runtime
• Identity & Access Management
• Tool gateway connectivity
• Agent memory
• Observability & Tracing
• Enterprise-grade scalability

To put it simply, MCP establishes how agents interact with tools, while AgentCore governs how agents are managed and used.

Such distinction matters since many enterprise-level AI initiatives struggle not with API integrations, but with monitoring, security, governance, and operability.

MCP vs AgentCore: Different Layers of the Technology Stack

One way to illustrate the difference between MCP and AgentCore is to liken the former to a USB port.

USB standardizes interactions between devices and computers.

It does not deal with the OS itself, users, permissions, or system monitoring.

Similarly,

• MCP standardizes tool interaction
• AgentCore is the platform that operates AI agents

That is why the two technologies complement each other rather than compete.

The reason many organizations consider them alternative solutions is a misconception caused by a lack of architectural understanding.

Does an MCP server log tokens?

Actually, there are two different kinds of tokens.

1. Authorization Tokens

This type includes OAuth access tokens or identity tokens, which are used to authenticate requests.

As an MCP server validates all incoming requests, it can log some valuable information like:

• The user identity
• Client application
• Scopes of access
• Timestamps of requests
• Tool usage
• Authorization decision

Organizations need not store any tokens. Instead, they should store secure identifiers and validated claims for auditing and regulatory compliance.

This allows:

• Log user activities
• Audit security
• Limit the number of requests
• Manage quotas
• Audit compliance

2. LLM Tokens

LLM tokens are the prompt and completion tokens used during inference in AI models.

These tokens correlate with AI model costs.

An MCP server normally cannot observe those tokens since the inference takes place elsewhere, in agent applications and runtimes.

An MCP server can observe LLM tokens only when it invokes an LLM directly.

AI Architecture Best Practices

Instead of incorporating governance within each MCP server, many firms deploy a central gateway.

The gateway is capable of:

• Authentication of the request
• Checking for permissions
• Applying quotas
• Telemetry collection
• Monitoring of usage
• Logging

This approach helps avoid duplication and achieve consistency across multiple MCP servers.

In AWS, AgentCore Gateway provides unified access to MCP servers and conventional APIs, serving as a central point of control for enterprise tool integration.

A Practical Architecture

There is often a combination of both types of architectures in practice.

Here is how it works:

1. A user makes an interaction with an AI agent.
2. The agent operates within Amazon Bedrock AgentCore.
3. Amazon AgentCore takes care of authentication, memory, observability, and execution of the agent.
4. Requests made by the tools go through the AgentCore Gateway.
5. Then, the gateway routes requests to the corresponding MCP servers.
6. MCP servers run the business logic via interactions with databases, CRMs, ticketing systems, or internal APIs.
7. The result comes back to the agent and to the user.

When to Use Each Technology?

MCP Server will be the optimal solution if you want standardized, reusable integrations with enterprise tools and data. This technology is best for situations where you have several AI clients but need them to share the same capabilities without creating separate APIs.

Amazon Bedrock AgentCore is the technology to use if you are deploying AI agents in production environments and need managed infrastructure, security, scalability, monitoring, and governance. In most cases, the optimal solution will be a combination of both.

Conclusion

The use of Enterprise AI is not merely restricted to interfacing language models with APIs; instead, it now requires architectures that strike the right balance between flexibility and governance due to the ability of AI agents to perform actual business processes.

Therefore, when put together, they create an excellent architecture MCP for establishing communication protocols, and AgentCore for reliability, security, and observability.

Drop a query if you have any questions regarding Amazon Bedrock AgentCore, and we will get back to you quickly.