The AWS Serverless Model Context Protocol (MCP) Server now supports specialized tools for AWS Lambda event source mappings (ESM), helping developers configure and manage ESMs more efficiently. These new tools combine the power of AI assistance with Lambda ESM expertise to streamline how developers set up, optimize, and troubleshoot event-driven serverless applications built on Lambda.
We previously launched the open-source Serverless MCP Server to enhance how developers build modern applications with AI-powered contextual guidance for architecture decisions, infrastructure provisioning, deployment automation, and troubleshooting of serverless applications. Starting today, we’re expanding the MCP server’s capabilities with new ESM tools that empower AI assistants, like Amazon Q Developer and Kiro, with proven knowledge of ESM patterns and best practices. The new ESM tools translate high-level throughput, latency, and reliability requirements into specific ESM configurations, generate complete AWS Serverless Application Model (AWS SAM) templates with optimized settings, validate network topology for Amazon Virtual Private Cloud (VPC)-based event sources, and diagnose common ESM issues. Thus, these tools enhance the event-driven application development experience, guiding developers through the entire ESM lifecycle, from initial setup to optimization and troubleshooting.
The key new ESM tools being added to the Serverless MCP Server are: the ESM guidance tool for contextual guidance across all supported event sources, the ESM optimization tool for analyzing configuration tradeoffs, and the ESM Kafka troubleshooting tool for specialized diagnostics with Amazon Managed Streaming for Apache Kafka (Amazon MSK) and self-managed Apache Kafka clusters.
To learn more about the Serverless MCP Server and how it can transform your AI-assisted application development, visit the launch blog post and documentation. To download and try out the open-source MCP server with your AI-enabled IDE of choice, visit the GitHub repository.
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Source: Amazon Web Services
