AWS Lambda MCP Server
A Model Context Protocol (MCP) server for AWS Lambda to select and run Lambda function as MCP tools without code changes.
Features
This MCP server acts as a bridge between MCP clients and AWS Lambda functions, allowing generative AI models to access and run Lambda functions as tools. This is useful, for example, to access private resources such as internal applications and databases without the need to provide public network access. This approach allows the model to use other AWS services, private networks, and the public internet.
graph LR
A[Model] <--> B[MCP Client]
B <--> C["MCP2Lambda<br>(MCP Server)"]
C <--> D[Lambda Function]
D <--> E[Other AWS Services]
D <--> F[Internet]
D <--> G[VPC]
style A fill:#f9f,stroke:#333,stroke-width:2px
style B fill:#bbf,stroke:#333,stroke-width:2px
style C fill:#bfb,stroke:#333,stroke-width:4px
style D fill:#fbb,stroke:#333,stroke-width:2px
style E fill:#fbf,stroke:#333,stroke-width:2px
style F fill:#dff,stroke:#333,stroke-width:2px
style G fill:#ffd,stroke:#333,stroke-width:2px
From a security perspective, this approach implements segregation of duties by allowing the model to invoke the Lambda functions but not to access the other AWS services directly. The client only needs AWS credentials to invoke the Lambda functions. The Lambda functions can then interact with other AWS services (using the function role) and access public or private networks.
Prerequisites
- Install
uv
from Astral or the GitHub README - Install Python using
uv python install 3.10
Installation
Here are some ways you can work with MCP across AWS, and we'll be adding support to more products including Amazon Q Developer CLI soon: (e.g. for Amazon Q Developer CLI MCP, ~/.aws/amazonq/mcp.json
):
{
"mcpServers": {
"awslabs.lambda-mcp-server": {
"command": "uvx",
"args": ["awslabs.lambda-mcp-server@latest"],
"env": {
"AWS_PROFILE": "your-aws-profile",
"AWS_REGION": "us-east-1",
"FUNCTION_PREFIX": "your-function-prefix",
"FUNCTION_LIST": "your-first-function, your-second-function",
"FUNCTION_TAG_KEY": "your-tag-key",
"FUNCTION_TAG_VALUE": "your-tag-value"
}
}
}
}
The AWS_PROFILE
and the AWS_REGION
are optional, their defualt values are default
and us-east-1
.
You can specify FUNCTION_PREFIX
, FUNCTION_LIST
, or both. If both are empty, all functions pass the name check.
After the name check, if both FUNCTION_TAG_KEY
and FUNCTION_TAG_VALUE
are set, functions are further filtered by tag (with key=value).
If only one of FUNCTION_TAG_KEY
and FUNCTION_TAG_VALUE
, then no function is selected and a warning is displayed.
IMPORTANT: The function name is used as MCP tool name. The function description in AWS Lambda is used as MCP tool description. The function description should clarify when to use the function (what it provides) and how (which parameters). For example, a function that gives access to an internal Customer Relationship Management (CRM) system can use this description:
Retrieve customer status on the CRM system based on { 'customerId' } or { 'customerEmail' }
Sample functions that can be deployed via AWS SAM are provided in the examples
folder.
Best practices
- Use the
FUNCTION_LIST
to specify the functions that are available as MCP tools. - Use the
FUNCTION_PREFIX
to specify the prefix of the functions that are available as MCP tools. - Use the
FUNCTION_TAG_KEY
andFUNCTION_TAG_VALUE
to specify the tag key and value of the functions that are available as MCP tools. - AWS Lambda
Description
property: the description of the function is used as MCP tool description, so it should be very detailed to help the model understand when and how to use the function and with with which parameters.
Security Considerations
When using this MCP server, you should consider:
- Only Lambda functions that are in the provided list or with a name starting with the prefix are imported as MCP tools.
- The MCP server needs permissions to invoke the Lambda functions.
- Each Lambda function has its own permissions to optionally access other AWS resources.