MCP Lambda SAM Server

The MCP Lambda SAM project delivers a fully serverless implementation of the Model Context Protocol (MCP) using AWS Lambda and the Server‑less Application Model (SAM). It addresses a common pain point for developers building AI assistants: the need to expose complex, dynamic toolsets and data resources over a lightweight, scalable interface without managing persistent infrastructure. By running MCP entirely on Lambda, the server automatically scales with traffic, eliminates the operational overhead of servers, and integrates seamlessly into existing AWS ecosystems.

At its core, the server exposes two distinct interfaces. The administrative interface allows system owners to register tools, resources, and prompts by storing metadata—including Lambda ARNs—in a DynamoDB registration table. Administrators also configure IAM permissions and network settings through SAM templates, ensuring that only authorized users can modify the MCP catalog. The client interface provides a simple SSE (Server‑Sent Events) endpoint for establishing long‑lived connections and a endpoint for sending commands. Clients receive streaming responses that include tool invocations, prompt completions, and resource lookups—all orchestrated by the MCP Lambda function.

Key capabilities of this implementation include dynamic tool invocation, session persistence via a dedicated DynamoDB session table, and fine‑grained IAM controls for both registration and usage endpoints. The system reads the registration table at startup and on each request, enabling hot‑reloading of new tools without redeploying the Lambda function. When a client issues a command that references a registered tool, the MCP Lambda dynamically invokes the corresponding Lambda function by ARN, passing along any required parameters. This architecture allows developers to add new capabilities—such as external API calls, database queries, or custom logic—simply by registering a new Lambda function.

Real‑world scenarios where MCP Lambda SAM shines include building conversational agents that need to perform on‑demand actions (e.g., booking a flight, querying inventory), creating modular AI workflows that can evolve over time, and deploying assistants in regulated environments where serverless isolation and IAM controls are mandatory. Because the server handles session state internally, developers can implement multi‑turn conversations without managing external caches or databases. The SSE interface also makes it straightforward to stream partial responses back to users, improving perceived latency and enabling real‑time feedback.

In summary, MCP Lambda SAM offers a low‑maintenance, highly scalable foundation for AI assistants. Its serverless nature eliminates operational overhead, while its dynamic registration and invocation model empowers developers to extend assistant capabilities on the fly. By integrating tightly with AWS IAM, DynamoDB, and Lambda, it provides a secure, observable, and developer‑friendly platform that accelerates the delivery of intelligent applications.