Discover 108 MCPs built for AWS.
Deploys a complete AWS backend infrastructure for retrieval-augmented generation applications, integrating with Gemini Pro and a Streamlit UI.
Enables AI assistants to execute SQL queries against AWS Athena databases and retrieve results.
Explore real-world projects and advanced implementations of agentic AI systems, multi-agent frameworks, RAG pipelines, and AI workflow automation.
Manages AWS cloud resources through a natural language chat interface.
Provides an interface to list log groups and read log entries from AWS CloudWatch, enabling AI assistants to access logging information.
Enables querying of AWS resources using boto3 code snippets via a Model Context Protocol (MCP) server.
Enables Large Language Models (LLMs) to search through AWS Lambda Powertools documentation across multiple runtimes.
Enables AI assistants to interact with and manage infrastructure-as-code operations for Ansible, Terraform, and LocalStack.
Implement a comprehensive, scalable machine learning inference architecture on Amazon EKS for deploying Large Language Models (LLMs) with agentic AI capabilities, including Retrieval Augmented Generation (RAG) and intelligent document processing.
Simplifies the deployment of Model Context Protocol (MCP) servers by providing a purpose-built CI/CD platform.
Query real-time AWS EC2 pricing data to find the cheapest instances based on CPU, RAM, and other specifications.
Establishes a comprehensive and scalable ML inference architecture on Amazon EKS, optimizing for cost-effective CPU inference with Graviton processors and accelerated GPU inference, while providing an end-to-end platform for deploying LLMs with agentic AI, RAG, and intelligent document processing.
Provides comprehensive AWS cost analysis and optimization recommendations based on proven best practices.
Implements a Model Context Protocol (MCP) server using AWS Lambda and SAM for serverless operation.
Connects AI assistants to DuckDB for powerful data analysis through the Model Context Protocol.
Enables AI agents, assistants, and chatbots to query Kubernetes Audit Logs through the Model Context Protocol (MCP).
Provides a unified interface to AWS services, enabling security teams and incident responders to investigate and analyze security incidents within their AWS environment.
Implements a reference authorization server based on the draft MCP OAuth 2.1 specification.
Extends AI assistant capabilities with example Model Context Protocol (MCP) servers written in Rust.
Indexes technical documentation on a local vector database and integrates with AI IDEs for semantic search.
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