MCP2Lambda

Run any AWS Lambda function as a Large Language Model (LLM) tool without code changes using Anthropic's Model Context Protocol (MCP).

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

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.

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.

The MCP server gives access to two tools:

1. The first tool can autodiscover all Lambda functions in your account that match a prefix or an allowed list of names. This tool shares the names of the functions and their descriptions with the model.

2. The second tool allows to invoke those Lambda functions by name passing the required parameters.

No code changes are required. You should change these configurations to improve results:

Strategy Selection

The gateway supports two different strategies for handling Lambda functions:

1. Pre-Discovery Mode (default: enabled): Registers each Lambda function as an individual tool at startup. This provides a more intuitive interface where each function appears as its own named tool.

2. Generic Mode: Uses two generic tools (list_lambda_functions and invoke_lambda_function) to interact with Lambda functions.

You can control this behavior through:

• Environment variable: PRE_DISCOVERY=true|false
• CLI flag: --no-pre-discovery (disables pre-discovery mode)

Example:

# Disable pre-discovery mode
export PRE_DISCOVERY=false
python main.py

# Or using CLI flag to disable pre-discovery
python main.py --no-pre-discovery

1. To provide the MCP client with the knowledge to use a Lambda function, the description of the Lambda function should indicate what the function does and which parameters it uses. See the sample functions for a quick demo and more details.

2. To help the model use the tools available via AWS Lambda, you can add something like this to your system prompt:

Use the AWS Lambda tools to improve your answers.

Overview

MCP2Lambda enables LLMs to interact with AWS Lambda functions as tools, extending their capabilities beyond text generation. This allows models to:

• Access real-time and private data, including data sources in your VPCs
• Execute custom code using a Lambda function as sandbox environment
• Interact with external services and APIs using Lambda functions internet access (and bandwidth)
• Perform specialized calculations or data proce

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