Metadata-Version: 2.4
Name: awslabs.lambda-mcp-server
Version: 0.1.0
Summary: An AWS Labs Model Context Protocol (MCP) server for AWS Lambda
Project-URL: Homepage, https://awslabs.github.io/mcp/
Project-URL: Documentation, https://awslabs.github.io/mcp/servers/lambda-mcp-server/
Project-URL: Source, https://github.com/awslabs/mcp.git
Project-URL: Bug Tracker, https://github.com/awslabs/mcp/issues
Project-URL: Changelog, https://github.com/awslabs/mcp/blob/main/src/lambda-mcp-server/CHANGELOG.md
Author: Amazon Web Services
Author-email: AWSLabs MCP <203918161+awslabs-mcp@users.noreply.github.com>
License: Apache-2.0
License-File: LICENSE
License-File: NOTICE
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: >=3.10
Requires-Dist: boto3>=1.37.27
Requires-Dist: mcp[cli]>=1.6.0
Requires-Dist: pydantic>=2.10.6
Description-Content-Type: text/markdown

# 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.

```mermaid
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

1. Install `uv` from [Astral](https://docs.astral.sh/uv/getting-started/installation/) or the [GitHub README](https://github.com/astral-sh/uv#installation)
2. 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`):

```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:

```plaintext
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` and `FUNCTION_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.
