Serverless MCP Framework

The Aws Serverless MCP framework turns a collection of AWS serverless resources into a fully‑featured Model Context Protocol (MCP) endpoint. By wiring Lambda functions, EventBridge rules and Step Functions into a single API Gateway, the server exposes an MCP‑compatible interface that can be queried by AI assistants such as Claude. This eliminates the need for bespoke back‑end code to expose tools, prompts or resources; instead, developers can declaratively add capabilities through AWS Parameter Store (SSM) and let the framework handle routing, authentication and event delivery.

At its core, the server solves the problem of contextual data access for AI assistants. When an assistant needs to call a custom tool, retrieve a prompt, or read a configuration file, it sends an MCP request to the endpoint. The framework forwards that request to the appropriate Lambda function or SSM parameter, performs any necessary authentication (JWT, OAuth, or API key), and returns the result in a standard MCP response. This tight integration means developers can focus on business logic while the server manages the plumbing between the assistant and AWS services.

Key capabilities include:

• Dynamic tool registration – Add new Lambda‑based tools by simply creating an SSM parameter under . The server automatically discovers and exposes them without redeploying code.
• Prompt and resource hosting – Store reusable prompts or configuration files in SSM, and expose them via the MCP or endpoints.
• EventBridge event handling – Start, stop and schedule tasks through EventBridge rules; a Step Function waits until a specified date before publishing events, enabling time‑based workflows.
• Flexible authentication – Support for API key, OAuth2 (issuer, authorization, token and revocation URLs), or custom authentication tokens ensures the server can be secured in any environment.
• Serverless scalability – Built on Lambda and API Gateway, the server scales automatically with request volume and incurs no idle costs.

Typical use cases involve building AI‑powered operational dashboards, automating DevOps pipelines, or creating conversational agents that can trigger cloud functions on demand. For example, a support chatbot could invoke an “update‑ticket” tool that writes to DynamoDB, while another tool fetches the latest configuration from SSM. The MCP server routes these calls seamlessly, allowing developers to add new functionality simply by adding a Lambda function and an SSM entry.

Integration into AI workflows is straightforward: developers configure the assistant’s MCP endpoint (e.g., ) and specify the authentication mechanism. The assistant then sends standard MCP requests, receives structured responses, and can chain multiple calls together to build complex interactions. Because the server is fully serverless, it fits naturally into modern cloud architectures and can be deployed once per project or environment, sharing the same codebase across multiple assistants.