MCP Proxy for AWS

The MCP Proxy for AWS package provides two ways to connect AI applications to MCP servers on AWS:

1. Using it as a proxy - It becomes a lightweight, client-side bridge between MCP clients (AI assistants like Claude Desktop, Kiro CLI) and MCP servers on AWS. (See MCP Proxy)
2. Using it as a library - Programmatically connect popular AI agent frameworks (LangChain, LlamaIndex, Strands Agents, etc.) to MCP servers on AWS. (See Programmatic Access)

• You want to connect to MCP servers on AWS (e.g., using Amazon Bedrock AgentCore) that use AWS IAM authentication (SigV4) instead of OAuth
• You're using MCP clients (like Claude Desktop, Kiro CLI) that don't natively support AWS IAM authentication
• You're building AI agents with popular frameworks like LangChain, Strands Agents, LlamaIndex, etc., that need to connect to MCP servers on AWS
• You want to avoid building custom SigV4 request signing logic yourself

The Problem: The official MCP specification supports OAuth-based authentication, but MCP servers on AWS can also use AWS IAM authentication (SigV4). Standard MCP clients don't know how to sign requests with AWS credentials.

The Solution: This package bridges that gap by:

• Handling SigV4 authentication automatically - Uses your local AWS credentials (from AWS CLI, environment variables, or IAM roles) to sign all MCP requests using SigV4
• Providing seamless integration - Works with existing MCP clients and frameworks
• Eliminating custom code - No need to build your own MCP client with SigV4 signing logic

Use as a proxy if you want to:

• Connect MCP clients like Claude Desktop or Kiro CLI to MCP servers on AWS with IAM credentials
• Add MCP servers on AWS to your AI assistant's configuration
• Use a command-line tool that runs as a bridge between your MCP client and AWS

Use as a library if you want to:

• Build AI agents programmatically using popular frameworks like LangChain, Strands Agents, or LlamaIndex
• Integrate AWS IAM-secured MCP servers directly into your Python applications
• Have fine-grained control over the MCP session lifecycle in your code

• Install Python 3.10+
• Install the uv package manager
• AWS credentials configured (via AWS CLI, environment variables, or IAM roles)
• (Optional, for docker users) Install Docker Desktop

The MCP Proxy serves as a lightweight, client-side bridge between MCP clients (AI assistants and developer tools) and IAM-secured MCP servers on AWS. The proxy handles SigV4 authentication using local AWS credentials and provides dynamic tool discovery.

# Run the server
uvx mcp-proxy-for-aws@latest <SigV4 MCP endpoint URL>

Note: The first run may take tens of seconds as uvx downloads and caches dependencies. Subsequent runs will start in seconds. Actual startup time depends on your network and hardware.

git clone https://github.com/aws/mcp-proxy-for-aws.git
cd mcp-proxy-for-aws
uv run mcp_proxy_for_aws/server.py <SigV4 MCP endpoint URL>

Docker images are published to the public AWS ECR registry.

You can use the pre-built image:

# Pull the latest image
docker pull public.ecr.aws/mcp-proxy-for-aws/mcp-proxy-for-aws:latest

# Or pull a specific version
docker pull public.ecr.aws/mcp-proxy-for-aws/mcp-proxy-for-aws:1.1.6

Or build the image locally:

# Build the Docker image
docker build -t mcp-proxy-for-aws .

Set the environment variables for the MCP Proxy for AWS:

# Credentials through profile
export AWS_PROFILE=<aws_profile>

# Credentials through parameters
export AWS_ACCESS_KEY_ID=<access_key_id>
export AWS_SECRET_ACCESS_KEY=<secret_access_key>
export AWS_SESSION_TOKEN=<session_token>

# AWS Region
export AWS_REGION=<aws_region>

Add the following configuration to your MCP client config file (e.g., for Kiro CLI, edit ~/.kiro/settings/mcp.json): Note Add your own endpoint by replacing <SigV4 MCP endpoint URL>

{
  "mcpServers": {
    "<mcp server name>": {
      "disabled": false,
      "type": "stdio",
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/mcp_proxy_for_aws",
        "run",
        "server.py",
        "<SigV4 MCP endpoint URL>",
        "--service",
        "<your service code>",
        "--profile",
        "default",
        "--region",
        "us-east-1",
        "--read-only",
        "--log-level",
        "INFO",
      ]
    }
  }
}

Using the pre-built public ECR image:

{
  "mcpServers": {
    "<mcp server name>": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--volume",
        "/full/path/to/.aws:/app/.aws:ro",
        "public.ecr.aws/mcp-proxy-for-aws/mcp-proxy-for-aws:latest",
        "<SigV4 MCP endpoint URL>"
      ],
      "env": {}
    }
  }
}

Or using a locally built image:

{
  "mcpServers": {
    "<mcp server name>": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "--volume",
        "/full/path/to/.aws:/app/.aws:ro",
        "mcp-proxy-for-aws",
        "<SigV4 MCP endpoint URL>"
      ],
      "env": {}
    }
  }
}

The MCP Proxy for AWS enables programmatic integration of IAM-secured MCP servers into AI agent frameworks. The library provides authenticated transport layers that work with popular Python AI frameworks.

By default, the library resolves AWS credentials automatically from the standard boto3 credential chain (environment variables, shared credentials file, etc.). You can optionally pass credentials programmatically via the credentials parameter. When provided, these take precedence over the aws_profile parameter. Note that aws_region must be explicitly specified when using credentials.

from botocore.credentials import Credentials
from mcp_proxy_for_aws.client import aws_iam_streamablehttp_client

creds = Credentials(access_key="...", secret_key="...", token="...")

mcp_client = aws_iam_streamablehttp_client(
    endpoint=mcp_url,
    aws_region=region,
    aws_service=service,
    credentials=creds,  # Optional: explicitly pass AWS credentials
)

The library supports two integration patterns depending on your framework:

Use with: Frameworks that accept a factory function that returns an MCP client, e.g. Strands Agents, Microsoft Agent Framework. The aws_iam_streamablehttp_client is passed as a factory to the framework, which handles the connection lifecycle internally.

Example - Strands Agents:

from mcp_proxy_for_aws.client import aws_iam_streamablehttp_client

mcp_client_factory = lambda: aws_iam_streamablehttp_client(
    endpoint=mcp_url,    # The URL of the MCP server
    aws_region=region,   # The region of the MCP server
    aws_service=service  # The underlying AWS service, e.g. "bedrock-agentcore"
)

with MCPClient(mcp_client_factory) as mcp_client:
    mcp_tools = mcp_client.list_tools_sync()
    agent = Agent(tools=mcp_tools, ...)

Example - Microsoft Agent Framework:

from mcp_proxy_for_aws.client import aws_iam_streamablehttp_client

mcp_client_factory = lambda: aws_iam_streamablehttp_client(
    endpoint=mcp_url,    # The URL of the MCP server
    aws_region=region,   # The region of the MCP server
    aws_service=service  # The underlying AWS service, e.g. "bedrock-agentcore"
)

mcp_tools = MCPStreamableHTTPTool(name="MCP Tools", url=mcp_url)
mcp_tools.get_mcp_client = mcp_client_factory

async with mcp_tools:
    agent = ChatAgent(tools=[mcp_tools], ...)

Use with: Frameworks that require direct access to the MCP sessions, e.g. LangChain, LlamaIndex. The aws_iam_streamablehttp_client provides the authenticated transport streams, which are then used to create an MCP ClientSession.

Example - LangChain:

from mcp_proxy_for_aws.client import aws_iam_streamablehttp_client

mcp_client = aws_iam_streamablehttp_client(
    endpoint=mcp_url,    # The URL of the MCP server
    aws_region=region,   # The region of the MCP server
    aws_service=service  # The underlying AWS service, e.g. "bedrock-agentcore"
)

async with mcp_client as (read, write, session_id_callback):
    async with ClientSession(read, write) as session:
        mcp_tools = await load_mcp_tools(session)
        agent = create_langchain_agent(tools=mcp_tools, ...)

Example - LlamaIndex:

from mcp_proxy_for_aws.client import aws_iam_streamablehttp_client

mcp_client = aws_iam_streamablehttp_client(
    endpoint=mcp_url,    # The URL of the MCP server
    aws_region=region,   # The region of the MCP server
    aws_service=service  # The underlying AWS service, e.g. "bedrock-agentcore"
)

async with mcp_client as (read, write, session_id_callback):
    async with ClientSession(read, write) as session:
        mcp_tools = await McpToolSpec(client=session).to_tool_list_async()
        agent = ReActAgent(tools=mcp_tools, ...)

Explore complete working examples for different frameworks in the ./examples/mcp-client directory:

Available examples:

• LangChain
• LlamaIndex
• Microsoft Agent Framework
• Strands Agents SDK

Run examples individually:

cd examples/mcp-client/[framework]  # e.g. examples/mcp-client/strands
uv run main.py

The client library is included when you install the package:

pip install mcp-proxy-for-aws

For development:

git clone https://github.com/aws/mcp-proxy-for-aws.git
cd mcp-proxy-for-aws
uv sync

We try to autodetect the service from the url, sometimes this fails, ensure that --service is set correctly to the service you are attempting to connect to. Otherwise the SigV4 signing will not be able to be verified by the service you connect to, resulting in this error. Also ensure that you have valid IAM credentials on your machine before retrying.

For long-running sessions, consider using long-lived credentials:

• Use an AWS profile via --profile
• Use IAM Identity Center and run aws sso login before starting the proxy

If your MCP client hangs waiting for a tool call response (e.g., due to expired credentials or an unresponsive endpoint), use --tool-timeout to set a maximum duration in seconds for each tool call. When the timeout is exceeded, the proxy returns a graceful error to the agent instead of hanging indefinitely.

For development setup, testing, and contribution guidelines, see:

• DEVELOPMENT.md - Development environment setup and testing
• CONTRIBUTING.md - How to contribute to this project

Resources to understand SigV4:

• SigV4 User Guide: https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_sigv.html
• SigV4 Signers: https://github.com/boto/botocore/blob/develop/botocore/signers.py
• SigV4a: https://github.com/aws-samples/sigv4a-signing-examples/blob/main/python/sigv4a_sign.py

Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License").

LLMs are non-deterministic and they make mistakes, we advise you to always thoroughly test and follow the best practices of your organization before using these tools on customer facing accounts. Users of this package are solely responsible for implementing proper security controls and MUST use AWS Identity and Access Management (IAM) to manage access to AWS resources. You are responsible for configuring appropriate IAM policies, roles, and permissions, and any security vulnerabilities resulting from improper IAM configuration are your sole responsibility. By using this package, you acknowledge that you have read and understood this disclaimer and agree to use the package at your own risk.