The project "spotinfo" is a command-line interface (CLI) tool designed for exploring and analyzing AWS EC2 Spot Instance inventory. It provides detailed insights into AWS Spot instance types, including real-time placement scores, pricing data, and interruption frequency, which are crucial for DevOps engineers and cloud infrastructure optimizers aiming to reduce costs and improve reliability. The tool offers real-time placement scores on a scale from 1 to 10, indicating the probability of successful instance launch, with regional and availability zone (AZ) level analysis enhanced by visual indicators such as green, yellow, and red emojis to represent score quality. It supports smart contextual scoring that reflects the likelihood of success for the entire request rather than individual instance types, and it optimizes data freshness with caching mechanisms. Spotinfo features advanced filtering capabilities using regex for instance type matching and multi-dimensional filters based on CPU, memory, price, regions, and placement scores. It supports cross-region comparisons and flexible sorting options by price, reliability, savings, or placement scores. The tool outputs data in multiple formats including human-readable tables with emoji indicators, plain text, JSON, and CSV, facilitating both manual inspection and automation scripting. The tool is resilient to network issues, offering embedded data for offline use and graceful fallbacks when AWS APIs are unavailable, while maintaining real-time API integration with intelligent caching. It supports multiple platforms including macOS, Linux, and Windows on AMD64 and ARM64 architectures. Importantly, spotinfo functions as a Model Context Protocol (MCP) server, enabling AI assistants to query AWS Spot Instance data through natural language commands. This integration allows users to interact with AWS Spot data seamlessly via AI tools like Claude Desktop, enhancing usability and automation. Comprehensive documentation is provided, covering usage, AWS Spot placement scores, examples, MCP server setup, and data sources. The project is open source under the Apache 2.0 License and includes CI/CD pipelines for testing, linting, and multi-architecture Docker builds.
https://github.com/alexei-led/spotinfo
Apache APISIX is a high-performance cloud-native API Gateway and AI Gateway that supports Model Context Protocol (MCP) integration through its mcp-bridge plugin, offering dynamic traffic management, security, and observability features.
AWS MCP Servers is a suite of specialized Model Context Protocol servers that integrate AWS best practices and resources into AI-powered development workflows to enhance cloud-native development and infrastructure management.
Webiny is an open-source, self-hosted headless CMS and framework, built on AWS serverless with AI-assisted development capabilities via an embedded MCP server, designed for large organizations.
MCP Registry is a community-driven centralized service offering a RESTful API for discovering, managing, and publishing Model Context Protocol (MCP) servers with MongoDB and Docker support.
Olares is an open-source personal cloud operating system that enables users to run local AI models and applications with personalized, context-aware AI interactions using a Model Context Protocol-like mechanism, providing a private and secure AI home server environment.
Cloudflare MCP Server is a suite of MCP servers that enable large language model clients to interact with and manage Cloudflare's cloud services through natural language commands, facilitating automation and insights across various domains.
mcp-grafana is an MCP server that provides comprehensive programmatic access to Grafana's dashboards, datasources, alerts, incidents, and on-call management features through MCP clients.
MCP Containers offers containerized versions of hundreds of MCP servers, enabling easy, secure, and up-to-date deployment of Model Context Protocol servers for AI integration and experimentation.