에이전트 강화
게시자의 자체 명령입니다. PlugMCP 엔드포인트가 아닙니다.
Host a copy with our keys and logs.
npx -y @aws/[email protected]Claude Code에는 로컬 명령용 설치 링크가 없습니다. 아래 명령을 복사해 터미널에서 실행합니다.
claude mcp add aws-run-mcp-servers-with-aws-lambda -- npx -y @aws/run-mcp-servers-with-aws-lambda@0.5.23터미널에서 실행합니다.
{ "mcpServers": { "aws-run-mcp-servers-with-aws-lambda": { "command": "npx", "args": [ "-y", "@aws/[email protected]" ] } }}claude_desktop_config.json
원격 URL인가요, 내 컴퓨터에서 실행하는 명령인가요? 두 설치 방식의 차이
PlugMCP에서 실행
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
실행게시자 제공
This project enables you to run Model Context Protocol stdio-based servers in AWS Lambda functions.
Currently, most implementations of MCP servers and clients are entirely local on a single machine. A desktop application such as an IDE or Claude Desktop initiates MCP servers locally as child processes and communicates with each of those servers over a long-running stdio stream.
Run Model Context Protocol (MCP) servers with AWS Lambda
flowchart LR
subgraph "Your Laptop"
Host["Desktop Application<br>with MCP Clients"]
S1["MCP Server A<br>(child process)"]
S2["MCP Server B<br>(child process)"]
Host <-->|"MCP Protocol<br>(over stdio stream)"| S1
Host <-->|"MCP Protocol<br>(over stdio stream)"| S2
end
This library helps you to wrap existing stdio MCP servers into Lambda functions. You can invoke these function-based MCP servers from your application using the MCP protocol over short-lived HTTPS connections. Your application can then be a desktop-based app, a distributed system running in the cloud, or any other architecture.
flowchart LR
subgraph "Distributed System"
App["Your Application<br>with MCP Clients"]
S3["MCP Server A<br>(Lambda function)"]
S4["MCP Server B<br>(Lambda function)"]
App <-->|"MCP Protocol<br>(over HTTPS connection)"| S3
App <-->|"MCP Protocol<br>(over HTTPS connection)"| S4
end
Using this library, the Lambda function will manage the lifecycle of your stdio MCP server. Each Lambda function invocation will:
This library supports connecting to Lambda-based MCP servers in four ways:
Many stdio-based MCP servers's documentation encourages using tools that download and run the server on-demand.
For example, uvx my-mcp-server or npx my-mcp-server.
These tools are often not pre-packaged in the Lambda environment, and it can be inefficient to
re-download the server on every Lambda invocation.
Instead, the examples in this repository show how to package the MCP server along with
the Lambda function code, then start it with python or node (or npx --offline) directly.
You will need to determine the right parameters depending on your MCP server's package. This can often be a trial and error process locally, since MCP server packaging varies.
Basic example:
from mcp.client.stdio import StdioServerParameters
server_params = StdioServerParameters(
command=sys.executable,
args=[
"-m",
"my_mcp_server_python_module",
"--my-server-command-line-parameter",
"some_value",
],
)
Locally, you would run this module using:
python -m my_mcp_server_python_module --my-server-command-line-parameter some_value
Other examples:
python -m mcpdoc.cli # Note the sub-module
python -c "from mcp_openapi_proxy import main; main()"
python -c "import asyncio; from postgres_mcp.server import main; asyncio.run(main())"
If you use Lambda layers, you need to also set the PYTHONPATH for the python sub-process:
lambda_paths = ["/opt/python"] + sys.path
env_config = {"PYTHONPATH": ":".join(lambda_paths)}
server_params = StdioServerParameters(
command=sys.executable,
args=[
"-c",
"from mcp_openapi_proxy import main; main()",
],
env=env_config,
)
Basic example:
const serverParams = {
command: "npx",
args: [
"--offline",
"my-mcp-server-typescript-module",
"--my-server-command-line-parameter",
"some_value",
],
};
Locally, you would run this module using:
npx --offline my-mcp-server-typescript-module --my-server-command-line-parameter some_value
Other examples:
node /var/task/node_modules/@ivotoby/openapi-mcp-server/bin/mcp-server.js
This library does not provide out-of-the-box mechanisms for managing any secrets needed by the wrapped MCP server. For example, the GitHub MCP server and the Brave search MCP server require API keys to make requests to third-party APIs. You may configure these API keys as encrypted environment variables in the Lambda function's configuration or retrieve them from Secrets Manager in the Lambda function code (examples below). However, note that anyone with access to invoke the Lambda function will then have access to use your API key to call the third-party APIs by invoking the function. We recommend limiting access to the Lambda function using least-privilege IAM policies. If you use an identity-based authentication mechanism such as OAuth, you could also store and retrieve API keys per user but there are no implementation examples in this repository.
import sys
import boto3
from mcp.client.stdio import StdioServerParameters
# Retrieve API key from Secrets Manager
secrets_client = boto3.client("secretsmanager")
api_key = secrets_client.get_secret_value(SecretId="my-api-key-secret")["SecretString"]
server_params = StdioServerParameters(
command=sys.executable,
args=["-m", "my_mcp_server"],
env={
"API_KEY": api_key,
},
)
import { SecretsManagerClient, GetSecretValueCommand } from "@aws-sdk/client-secrets-manager";
const secretsClient = new SecretsManagerClient({});
const secret = await secretsClient.send(
new GetSecretValueCommand({ SecretId: "my-api-key-secret" })
);
const apiKey = secret.SecretString;
const serverParams = {
command: "npx",
args: ["--offline", "my-mcp-server"],
env: {
API_KEY: apiKey,
},
};
If your MCP server needs to call AWS APIs (such as the MCP servers for AWS), you can pass the Lambda function's AWS credentials to the wrapped MCP server via environment variables. The wrapped MCP server's child process does not automatically inherit the Lambda execution role's credentials. Again, note that anyone with access to invoke the Lambda function will then have access to use the function's AWS credentials to call AWS APIs by invoking the function. We recommend limiting access to the Lambda function using least-privilege IAM policies.
import os
import sys
import boto3
from mcp.client.stdio import StdioServerParameters
# Get AWS credentials from Lambda execution role to pass to subprocess
session = boto3.Session()
credentials = session.get_credentials()
if credentials is None:
raise RuntimeError("Unable to retrieve AWS credentials from the execution environment")
resolved = credentials.get_frozen_credentials()
server_params = StdioServerParameters(
command=sys.executable,
args=["-m", "my_mcp_server"],
env={
"AWS_REGION": os.environ.get("AWS_REGION", "us-west-2"),
"AWS_DEFAULT_REGION": os.environ.get("AWS_REGION", "us-west-2"),
"AWS_ACCESS_KEY_ID": resolved.access_key,
"AWS_SECRET_ACCESS_KEY": resolved.secret_key,
"AWS_SESSION_TOKEN": resolved.token or "",
},
)
Some MCP servers require an AWS profile and do not support credentials passed via environment variables. In this case, you can write the credentials to a file and point the MCP server to it.
import os
import sys
import boto3
from mcp.client.stdio import StdioServerParameters
# Get AWS credentials from Lambda execution role to pass to subprocess
session = boto3.Session()
credentials = session.get_credentials()
if credentials is None:
raise RuntimeError("Unable to retrieve AWS credentials from the execution environment")
resolved = credentials.get_frozen_credentials()
# Write credentials to disk as default profile
aws_dir = "/tmp/.aws"
os.makedirs(aws_dir, exist_ok=True)
with open(f"{aws_dir}/credentials", "w") as f:
f.write("[default]\n")
f.write(f"aws_access_key_id = {resolved.access_key}\n")
f.write(f"aws_secret_access_key = {resolved.secret_key}\n")
if resolved.token:
f.write(f"aws_session_token = {resolved.token}\n")
server_params = StdioServerParameters(
command=sys.executable,
args=["-m", "my_mcp_server"],
env={
"AWS_REGION": os.environ.get("AWS_REGION", "us-west-2"),
"AWS_DEFAULT_REGION": os.environ.get("AWS_REGION", "us-west-2"),
"AWS_SHARED_CREDENTIALS_FILE": f"{aws_dir}/credentials",
},
)
See a full, deployable example here.
flowchart LR
App["MCP Client"]
T1["MCP Server<br>(Lambda function)"]
T2["API Gateway"]
T3["OAuth Server<br>(Cognito or similar)"]
App -->|"MCP Streamable<br>HTTP Transport"| T2
T2 -->|"Invoke"| T1
T2 -->|"Authorize"| T3
This solution is compatible with most MCP clients that support the streamable HTTP transport. MCP servers deployed with this architecture can typically be used with off-the-shelf MCP-compatible applications such as Cursor, Cline, Claude Desktop, etc.
You can choose your desired OAuth server provider for this solution. The examples in this repository use Amazon Cognito, or you can use third-party providers such as Okta or Auth0 with API Gateway custom authorization.
import sys
from mcp.client.stdio import StdioServerParameters
from mcp_lambda import APIGatewayProxyEventHandler, StdioServerAdapterRequestHandler
server_params = StdioServerParameters(
command=sys.executable,
args=[
"-m",
"my_mcp_server_python_module",
"--my-server-command-line-parameter",
"some_value",
],
)
request_handler = StdioServerAdapterRequestHandler(server_params)
event_handler = APIGatewayProxyEventHandler(request_handler)
def handler(event, context):
return event_handler.handle(event, context)
See a full, deployable example here.
import {
Handler,
Context,
APIGatewayProxyWithCognitoAuthorizerEvent,
APIGatewayProxyResult,
} from "aws-lambda";
import {
APIGatewayProxyEventHandler,
StdioServerAdapterRequestHandler,
} from "@aws/run-mcp-servers-with-aws-lambda";
const serverParams = {
command: "npx",
args: [
"--offline",
"my-mcp-server-typescript-module",
"--my-server-command-line-parameter",
"some_value",
],
};
const requestHandler = new APIGatewayProxyEventHandler(
new StdioServerAdapterRequestHandler(serverParams)
);
export const handler: Handler = async (
event: APIGatewayProxyWithCognitoAuthorizerEvent,
context: Context
): Promise<APIGatewayProxyResult> => {
return requestHandler.handle(event, context);
};
See a full, deployable example here.
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
# Create OAuth client provider here
async with streamablehttp_client(
url="https://abc123.execute-api.us-west-2.amazonaws.com/prod/mcp",
auth=oauth_client_provider,
) as (
read_stream,
write_stream,
_,
):
async with ClientSession(read_stream, write_stream) as session:
await session.initialize()
tool_result = await session.call_tool("echo", {"message": "hello"})
See a full example as part of the sample chatbot here.
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
const client = new Client(
{
name: "my-client",
version: "0.0.1",
},
{
capabilities: {
sampling: {},
},
}
);
// Create OAuth client provider here
const transport = new StreamableHTTPClientTransport(
"https://abc123.execute-api.us-west-2.amazonaws.com/prod/mcp",
{
authProvider: oauthProvider,
}
);
await client.connect(transport);
See a full example as part of the sample chatbot here.
flowchart LR
App["MCP Client"]
T1["MCP Server<br>(Lambda function)"]
T2["Bedrock AgentCore Gateway"]
T3["OAuth Server<br>(Cognito or similar)"]
App -->|"MCP Streamable<br>HTTP Transport"| T2
T2 -->|"Invoke"| T1
T2 -->|"Authorize"| T3
This solution is compatible with most MCP clients that support the streamable HTTP transport. MCP servers deployed with this architecture can typically be used with off-the-shelf MCP-compatible applications such as Cursor, Cline, Claude Desktop, etc.
You can choose your desired OAuth server provider with Bedrock AgentCore Gateway, such as Amazon Cognito, Okta, or Auth0.
Using Bedrock AgentCore Gateway in front of your stdio-based MCP server requires that you retrieve the MCP server's tool schema, and provide it in the AgentCore Gateway Lambda target configuration. AgentCore Gateway can then advertise the schema to HTTP clients and validate request inputs and outputs.
To retrieve and save your stdio-based MCP server's tool schema to a file, run:
npx @modelcontextprotocol/inspector --cli --method tools/list <your MCP server command and arguments> > tool-schema.json
# For example:
npx @modelcontextprotocol/inspector --cli --method tools/list uvx mcp-server-time > tool-schema.json
Some MCP servers generate tool schemas that AgentCore Gateway rejects with strict validation,
such as "items": {}, "default": null, or anyOf with {"type": "null"}.
You may need to clean up the schema before using it:
python3 scripts/clean-tool-schema.py tool-schema.json
import sys
from mcp.client.stdio import StdioServerParameters
from mcp_lambda import BedrockAgentCoreGatewayTargetHandler, StdioServerAdapterRequestHandler
server_params = StdioServerParameters(
command=sys.executable,
args=[
"-m",
"my_mcp_server_python_module",
"--my-server-command-line-parameter",
"some_value",
],
)
request_handler = StdioServerAdapterRequestHandler(server_params)
event_handler = BedrockAgentCoreGatewayTargetHandler(request_handler)
def handler(event, context):
return event_handler.handle(event, context)
See a full, deployable example here.
import { Handler, Context } from "aws-lambda";
import {
BedrockAgentCoreGatewayTargetHandler,
StdioServerAdapterRequestHandler,
} from "@aws/run-mcp-servers-with-aws-lambda";
const serverParams = {
command: "npx",
args: [
"--offline",
"my-mcp-server-typescript-module",
"--my-server-command-line-parameter",
"some_value",
],
};
const requestHandler = new BedrockAgentCoreGatewayTargetHandler(
new StdioServerAdapterRequestHandler(serverParams)
);
export const handler: Handler = async (
event: Record<string, unknown>,
context: Context
): Promise<Record<string, unknown>> => {
return requestHandler.handle(event, context);
};
See a full, deployable example here.
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
# Create OAuth client provider here
async with streamablehttp_client(
url="https://abc123.gateway.bedrock-agentcore.us-west-2.amazonaws.com/mcp",
auth=oauth_client_provider,
) as (
read_stream,
write_stream,
_,
):
async with ClientSession(read_stream, write_stream) as session:
await session.initialize()
tool_result = await session.call_tool("echo", {"message": "hello"})
See a full example as part of the sample chatbot here.
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
const client = new Client(
{
name: "my-client",
version: "0.0.1",
},
{
capabilities: {
sampling: {},
},
}
);
// Create OAuth client provider here
const transport = new StreamableHTTPClientTransport(
"https://abc123.gateway.bedrock-agentcore.us-west-2.amazonaws.com/mcp",
{
authProvider: oauthProvider,
}
);
await client.connect(transport);
See a full example as part of the sample chatbot here.
flowchart LR
App["MCP Client"]
T1["MCP Server<br>(Lambda function)"]
T2["Lambda function URL"]
App -->|"Custom Streamable HTTP<br>Transport with AWS Auth"| T2
T2 -->|"Invoke"| T1
This solution uses AWS IAM for authentication, and relies on granting Lambda InvokeFunctionUrl permission to your IAM users and roles to enable access to the MCP server. Clients must use an extension to the MCP Streamable HTTP transport that signs requests with AWS SigV4. Off-the-shelf MCP-compatible applications are unlikely to have support for this custom transport, so this solution is more appropriate for service-to-service communication rather than for end users.
import sys
from mcp.client.stdio import StdioServerParameters
from mcp_lambda import LambdaFunctionURLEventHandler, StdioServerAdapterRequestHandler
server_params = StdioServerParameters(
command=sys.executable,
args=[
"-m",
"my_mcp_server_python_module",
"--my-server-command-line-parameter",
"some_value",
],
)
request_handler = StdioServerAdapterRequestHandler(server_params)
event_handler = LambdaFunctionURLEventHandler(request_handler)
def handler(event, context):
return event_handler.handle(event, context)
See a full, deployable example here.
import {
Handler,
Context,
APIGatewayProxyEventV2WithIAMAuthorizer,
APIGatewayProxyResultV2,
} from "aws-lambda";
import {
LambdaFunctionURLEventHandler,
StdioServerAdapterRequestHandler,
} from "@aws/run-mcp-servers-with-aws-lambda";
const serverParams = {
command: "npx",
args: [
"--offline",
"my-mcp-server-typescript-module",
"--my-server-command-line-parameter",
"some_value",
],
};
const requestHandler = new LambdaFunctionURLEventHandler(
new StdioServerAdapterRequestHandler(serverParams)
);
export const handler: Handler = async (
event: APIGatewayProxyEventV2WithIAMAuthorizer,
context: Context
): Promise<APIGatewayProxyResultV2> => {
return requestHandler.handle(event, context);
};
See a full, deployable example here.
from mcp import ClientSession
from mcp_proxy_for_aws.client import aws_iam_streamablehttp_client
async with aws_iam_streamablehttp_client(
endpoint="https://url-id-12345.lambda-url.us-west-2.on.aws",
aws_service="lambda",
aws_region="us-west-2",
) as (
read_stream,
write_stream,
_,
):
async with ClientSession(read_stream, write_stream) as session:
await session.initialize()
tool_result = await session.call_tool("echo", {"message": "hello"})
See a full example as part of the sample chatbot here.
import { StreamableHTTPClientWithSigV4Transport } from "@aws/run-mcp-servers-with-aws-lambda";
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
const client = new Client(
{
name: "my-client",
version: "0.0.1",
},
{
capabilities: {
sampling: {},
},
}
);
const transport = new StreamableHTTPClientWithSigV4Transport(
new URL("https://url-id-12345.lambda-url.us-west-2.on.aws"),
{
service: "lambda",
region: "us-west-2",
}
);
await client.connect(transport);
See a full example as part of the sample chatbot here.
flowchart LR
App["MCP Client"]
T1["MCP Server<br>(Lambda function)"]
App -->|"Custom MCP Transport<br>(Lambda Invoke API)"| T1
Like the Lambda function URL approach, this solution uses AWS IAM for authentication. It relies on granting Lambda InvokeFunction permission to your IAM users and roles to enable access to the MCP server. Clients must use a custom MCP transport that directly calls the Lambda Invoke API. Off-the-shelf MCP-compatible applications are unlikely to have support for this custom transport, so this solution is more appropriate for service-to-service communication rather than for end users.
import sys
from mcp.client.stdio import StdioServerParameters
from mcp_lambda import stdio_server_adapter
server_params = StdioServerParameters(
command=sys.executable,
args=[
"-m",
"my_mcp_server_python_module",
"--my-server-command-line-parameter",
"some_value",
],
)
def handler(event, context):
return stdio_server_adapter(server_params, event, context)
See a full, deployable example here.
import { Handler, Context } from "aws-lambda";
import { stdioServerAdapter } from "@aws/run-mcp-servers-with-aws-lambda";
const serverParams = {
command: "npx",
args: [
"--offline",
"my-mcp-server-typescript-module",
"--my-server-command-line-parameter",
"some_value",
],
};
export const handler: Handler = async (event, context: Context) => {
return await stdioServerAdapter(serverParams, event, context);
};
See a full, deployable example here.
from mcp import ClientSession
from mcp_lambda import LambdaFunctionParameters, lambda_function_client
server_params = LambdaFunctionParameters(
function_name="my-mcp-server-function",
region_name="us-west-2",
)
async with lambda_function_client(server_params) as (
read_stream,
write_stream,
):
async with ClientSession(read_stream, write_stream) as session:
await session.initialize()
tool_result = await session.call_tool("echo", {"message": "hello"})
See a full example as part of the sample chatbot here.
import {
LambdaFunctionParameters,
LambdaFunctionClientTransport,
} from "@aws/run-mcp-servers-with-aws-lambda";
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
const serverParams: LambdaFunctionParameters = {
functionName: "my-mcp-server-function",
regionName: "us-west-2",
};
const client = new Client(
{
name: "my-client",
version: "0.0.1",
},
{
capabilities: {
sampling: {},
},
}
);
const transport = new LambdaFunctionClientTransport(serverParams);
await client.connect(transport);
See a full example as part of the sample chatbot here.
See the development guide for instructions to deploy and run the examples in this repository.
See CONTRIBUTING for more information.
This project is licensed under the Apache-2.0 License.
Run Model Context Protocol (MCP) servers with AWS Lambda
{ "mcpServers": { "aws-run-mcp-servers-with-aws-lambda": { "command": "npx", "args": [ "-y", "@aws/[email protected]" ] } }}도구
아직 도구 목록이 없습니다. 원격 서버는 첫 상태 확인 후에 도구 목록이 생깁니다.
FAQ
이 페이지에서 Cursor, Claude Desktop, Claude Code 또는 VS Code용 stdio 설정을 복사합니다. PlugMCP 프록시가 아니라 게시자의 설치 방식을 사용합니다.
이 레코드에는 필수 환경 변수가 등록되어 있지 않습니다.
@aws/run MCP Servers With Aws Lambda은(는) stdio를 통해 로컬에서 실행됩니다.
마지막 확인: 2026-11-14. Run Model Context Protocol (MCP) servers with AWS Lambda