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You can deploy MCP tools on ECS Fargate with AWS CDK, but first decide which Claude architecture you mean. This guide follows the AWS Samples CDK design: a separate agent service calls Claude Sonnet 4 through Amazon Bedrock and communicates with MCP server services on ECS. Claude itself does not run inside the Fargate containers. If instead you want Claude’s hosted clients to connect directly to your custom MCP server, you need a publicly reachable HTTPS endpoint and a different ingress and security design.
Choose how Claude will reach your MCP tools
MCP is a protocol that lets compatible AI applications use tools and data exposed by a server. It does not host the AI model. An ECS task running an MCP server is therefore not, by itself, a Claude deployment.
| Design | Where the model runs | Network path | Best fit |
|---|---|---|---|
| Bedrock-backed agent (the CDK sample in this guide) | Claude Sonnet 4 is accessed as a hosted model through Amazon Bedrock; the AI agent and MCP servers run as ECS services. | The agent and MCP services communicate within the VPC using ECS Service Connect. The sample also includes an Application Load Balancer. | An application you operate where the agent, MCP tools, and AWS resources are part of your own AWS deployment. |
| Claude remote custom connector | Claude runs in Anthropic’s hosted client environment and calls your remote MCP server. | The MCP endpoint must be reachable from the public internet and Anthropic’s IP ranges. A private-VPC-only endpoint will not work for this connector flow. | Users connect a Claude hosted client directly to a custom remote MCP server. |
| AWS-managed ECS MCP service | An assistant such as Claude Code uses AWS’s managed integration to inspect and troubleshoot ECS. | Uses AWS IAM permissions and MCP Proxy for AWS to sign requests with SigV4. | ECS operations, rather than deploying your own general-purpose MCP server. |
The distinction matters for network design: an MCP service that is deliberately private can work for an agent inside the VPC, but it cannot serve as the endpoint for Anthropic’s remote connector. Anthropic’s Help Center explains that “When you add a custom connector, Claude connects to your remote MCP server from Anthropic’s cloud infrastructure, rather than from your local device.”
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The closest documented match for CDK, Fargate, and Claude is an AWS Samples repository. Its architecture includes an AI service configured to access Claude Sonnet 4 through Bedrock, a custom Python MCP server, and an AWS API MCP server. ECS Service Connect supports service-to-service communication; an Application Load Balancer is also part of the example.
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The sample’s AWS API MCP server is restricted in the example to listing S3 buckets. It also creates an API-key secret and uses an x-api-key header in its load-balancer example. Those are sample implementation details, not universal requirements or a complete production security design. Review the repository’s current code and configuration before adapting it.
Check prerequisites before deploying
The CDK sample README lists these prerequisites:
- Node.js 20 or later.
- Docker.
- A configured AWS CLI.
- Access to the relevant Bedrock model in the AWS account and Region you plan to use.
The repository instructions also call for installing dependencies, logging in to public Amazon ECR, bootstrapping the AWS environment, and deploying with npm run cdk deploy. Follow the README for the precise setup commands and configuration expected by the version you check out; the available documentation summary does not establish a single repository URL, dependency command, account/Region selection, or configuration file to use.
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A separate AWS ECS walkthrough estimates 30–40 minutes for its own CloudFormation and Amazon Nova 2 Lite example. That is not a time estimate for this CDK/Claude sample. The CDK sample README reports 362.32 seconds as example deployment output; AWS Samples does not establish that as a benchmark or promised deployment duration.
Deploy the CDK sample
- Review the repository and select the target environment. Check the sample’s README and code, then confirm the AWS account and Region, available Bedrock model access, and the AWS permissions required by its stack. Do not assume that sample permissions are appropriate for production.
- Install the documented dependencies. Use the dependency installation instructions in the repository. Confirm Node.js 20 or later and Docker are available, and that the AWS CLI is configured for the intended account.
- Prepare the container image workflow. Follow the repository’s instructions to log in to public ECR and build or otherwise prepare the required images. Docker is a stated prerequisite; image names, tags, and build commands should come from the checked-out README rather than be guessed.
- Bootstrap the CDK environment. Run the bootstrap procedure specified by the sample for the account and Region where you will deploy. CDK bootstrap setup is environment-specific, so verify the target before proceeding.
- Deploy the stack. From the location specified by the README, run
npm run cdk deploy. Review the CDK changes and outputs, and confirm the stack deployed the expected ECS services, load balancer, and Service Connect configuration. - Verify the service path. Check ECS service and task health, then test the documented application and MCP flow using the sample’s own endpoint and authentication instructions. A healthy task alone does not prove the agent can reach the MCP servers or that Bedrock model access is configured correctly.
The sample README’s summarized instructions do not specify the exact validation request, expected response, or cleanup command. Use the current repository documentation for those actions rather than inventing an endpoint or test payload.
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Adapt the network design for direct Claude connectors
If the requirement is for Claude’s hosted clients to connect to your server, treat the MCP service as an internet-facing service, not as a private-only service behind an internal route. Anthropic says connector requests come from its cloud infrastructure and that the server must be reachable from Anthropic’s IP ranges. Teams that filter ingress should consult Anthropic’s current published ranges and connector documentation when configuring their firewall.
- Provide a publicly reachable HTTPS endpoint for the MCP server; a private address accessible only within your VPC is incompatible with this connector flow.
- Configure the authentication method supported by the connector setup, such as an appropriate sign-in flow or fixed request headers, and independently authorize which tools and data each caller may use.
- Keep downstream AWS permissions on the MCP task role narrowly scoped. Authentication to the MCP endpoint does not constrain what that role can access.
- For a multi-user or multi-tenant service, design tenant separation and authorization explicitly so one caller cannot use another tenant’s data or permissions.
AWS Prescriptive Guidance treats remote MCP hosting as a way to centrally manage access, authentication and authorization, versioning, and updates. It also emphasizes both sides of the permission boundary: agent-to-server access and the server’s access to downstream resources.
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Keep permissions narrow across every route
The sample’s bucket-list-only AWS API permissions are a useful illustration of constraining a tool, not a default policy for your own service. Define the minimum downstream AWS actions and resources each server needs, and assign only those permissions to its execution role. Where different tools have different authority, avoid giving every tool the same broad role simply because they share a deployment.
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MCP-specific controls do not necessarily govern every action an AI assistant can take. AWS security guidance warns that an assistant with shell or AWS CLI access may call AWS APIs directly, bypassing MCP controls. Least-privilege IAM and broader AWS account or organization guardrails therefore remain important even when tool calls normally pass through MCP.
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Plan for sessions and horizontal scaling
AWS’s ECS walkthrough uses Streamable HTTP for its MCP server. It notes that stateless Streamable HTTP can support horizontal replication without session affinity, while stateful workflows may use Mcp-Session-Id and require session behavior to be planned. This is an architectural reference, not proof that the CDK/Claude sample uses the same transport or session configuration. Verify the transport and state model in the server you deploy before adding replicas or relying on load-balancer behavior.
Know when a managed ECS MCP service is a better fit
AWS separately documents a managed Amazon ECS MCP server that can integrate with assistants such as Claude Code. It is currently described as preview and subject to change. It uses IAM permissions and MCP Proxy for AWS to sign requests with SigV4, and provides tools for inspecting and troubleshooting ECS workloads. It is an alternative for operating ECS, not a way to deploy your own MCP server to Fargate with CDK.
Quick Recap
Deployment decision checklist
- Choose Bedrock-backed Claude when your deployed agent should call Claude through AWS and reach MCP services within your VPC.
- Choose a remote connector topology when Claude’s hosted clients must call your custom server; design for public HTTPS reachability from Anthropic’s infrastructure.
- Separate endpoint authentication from downstream AWS authorization, and scope task roles and tool permissions to the minimum required.
- Use the AWS Samples CDK repository as a starting point, not as a production security blueprint; inspect its current implementation before adopting it.
- Consider the preview managed ECS MCP service only when the actual need is assistant-driven ECS inspection and troubleshooting.
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