MCP2Lambda is a Python MCP server that turns existing AWS Lambda functions into tools a large language model can call, without changing the function code. It sits between an MCP client and the Lambda service: the model asks the server to run a function, the server invokes it with the configured AWS credentials, and the function does the actual work against other AWS services, a private VPC or the public internet. The README frames this as segregation of duties, because the client only needs permission to invoke Lambda while the function role holds the broader access. It also makes private resources such as internal applications and databases reachable without giving them public network access. Two strategies are supported. Pre-discovery mode, enabled by default, enumerates matching functions at startup and registers each one as its own named tool. Generic mode instead exposes two tools, list_lambda_functions and invoke_lambda_function, and the model picks a function by name at call time. The behaviour is controlled by the PRE_DISCOVERY environment variable or the --no-pre-discovery flag. By default only functions whose name begins with mcp2lambda- are visible, and the description of each Lambda function is what tells the model what the tool does and which parameters it takes, so those descriptions have to be written with the model in mind. The project suggests adding a line to the system prompt telling the model to use the Lambda tools. It requires Python 3.12 or newer and configured AWS credentials, works with MCP clients such as Claude Desktop and with Amazon Bedrock applications using the Converse API, and can be installed manually or through Smithery. Three sample Lambda functions are included to demonstrate simple lookup tools that hold basic permissions and can only write to CloudWatch logs. It targets AWS teams that already run Lambda functions and want to reuse them as model tools.
https://github.com/danilop/MCP2Lambda
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