Streamlining AI Agent Integration with MCP Server Support
Amazon Web Services (AWS) has announced a significant enhancement to its Amazon Bedrock AgentCore Gateway, enabling support for existing Model Context Protocol (MCP) servers as a new target type. This innovation helps organizations consolidate multiple MCP servers—custom-built, open-source, or publicly available—under a unified interface, reducing operational complexity and enhancing tool discovery, access, and security.
Why MCP Server Integration Matters
As organizations scale their use of AI agents, developers increasingly build specialized MCP servers for different functions such as shopping cart management, product search, and promotions. Previously, agents had to connect to each server separately, which involved managing multiple authentication layers and gateway configurations. The new integration allows these servers to be grouped behind a single AgentCore Gateway, streamlining access and management.
This unified approach simplifies the deployment and maintenance of AI agents while preserving team-specific ownership and access controls. Teams can organize their MCP servers based on business units, product features, or security requirements, while the gateway manages authentication and tool discovery across the board.
Architectural Flexibility and Tool Management
AgentCore Gateway serves as an integration hub, supporting various tool types including AWS Lambda functions, REST APIs, Smithy models, and now MCP servers. This flexibility allows organizations to maintain existing investments while gradually transitioning to MCP-native implementations.
The gateway decouples inbound authentication from the target systems, enabling agents to interact with tools using different identity providers through a centralized interface. This results in simplified development, deployment, and security management.
Enhancing Tool Discovery and Invocation
Once integrated, MCP server tools appear alongside other tools in the gateway’s catalog. The gateway handles naming collisions, provides semantic search across tools, and manages all protocol translations and authentication flows. This ensures a consistent interface for agents, regardless of the underlying tool implementation.
During tool invocation, the gateway establishes secure sessions with MCP servers, retrieves necessary credentials, and carries out the execution seamlessly. This architecture supports both implicit and explicit synchronization of tool schemas to keep definitions up-to-date.
Implementing MCP Server Targets
To get started, users create an AgentCore Gateway and add MCP servers as targets using interfaces such as the AWS SDK for Python (Boto3), AWS Management Console, or AWS CLI. The setup includes configuring authentication through Amazon Cognito or another OAuth 2.0 provider.
A sample MCP server can be built using FastMCP with stateless HTTP transport. Once deployed to AgentCore Runtime, it can be registered with the gateway as a target. The gateway uses OAuth credentials from an Identity Resource Credential Provider to authenticate with the server.
Testing and Synchronizing Tools
Users can test gateway functionality using frameworks like Strands Agents. The tools can be discovered and invoked through semantic queries or direct calls. AWS provides APIs for on-demand synchronization of MCP target tools, ensuring that the gateway maintains an accurate and current list of available functionalities.
The SynchronizeGatewayTargets API allows administrators to refresh tool definitions after changes. This avoids the latency and reliability issues of real-time tool discovery, instead using a cache-first model for high performance and consistency.
Search and Execution Capabilities
AgentCore Gateway supports semantic search by generating embeddings for tool metadata during synchronization. This enables agents to discover tools based on intent, even when exact keywords are not used. Execution of tools is handled through the tools/call operation, which involves real-time communication with the MCP server and proper session initialization.
Authentication is managed centrally, and the gateway ensures that valid credentials are used during execution, even if the cached tool definitions originated from a previous session. This design supports secure and efficient tool invocation across different MCP environments.
Conclusion
The addition of MCP server support as a target type in Amazon Bedrock AgentCore Gateway marks a major step forward in enterprise AI architecture. It enables organizations to unify their tool management strategies, reduce operational overhead, and enhance security across various system components. With support for traditional APIs, serverless functions, and now MCP servers, AgentCore Gateway becomes a powerful foundation for building and scaling agent-based applications.
For more information, visit the GitHub repository or consult the Amazon Bedrock AgentCore Gateway Developer Guide.
This article is inspired by content from Original Source. It has been rephrased for originality. Images are credited to the original source.