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Why this server?
Enables natural language interaction with Azure services through Claude Desktop, supporting resource management, subscription handling, and tenant selection with secure authentication. This can be adapted to GCP.
Why this server?
A Model Context Protocol server that integrates with AWS CodePipeline, allowing users to manage pipelines through Windsurf and Cascade using natural language commands. This can be adapted to GCP.
Why this server?
A Model Context Protocol server that integrates with Amazon Braket, allowing AI assistants to access, control, and interpret results from quantum computing resources. While specific to Amazon Braket, it demonstrates the capability to manage cloud resources.
Why this server?
A command-line interface and API that allows users to analyze and visualize AWS cloud spending data by enabling Claude to query AWS Cost Explorer through natural language conversations. It can inspire similar tools for GCP cost management.
Why this server?
TypeScript implementation of Kubernetes cluster operations for pods, deployments, services. Kubernetes is relevant for PaaS setup and configuration.
Why this server?
This project is intended as a both MCP server connecting to Kubernetes and a library to build more servers for any custom resources in Kubernetes. Kubernetes is relevant for PaaS setup and configuration.
Why this server?
A powerful and flexible Kubernetes MCP server implementation with support for OpenShift. Kubernetes is relevant for PaaS setup and configuration.
Why this server?
A Model Context Protocol (MCP) server that provides intelligent access to PowerPlatform/Dataverse entities and records. This tool offers context-aware assistance, entity exploration and metadata access, analogous to managing GCP resources.
Why this server?
Run SQL queries with AWS Athena to access data available from AWS Glue catalog. Useful to create a tool like this for GCP.
Why this server?
A Model Context Protocol server that enables Claude to execute Python code using boto3 to query and manage AWS resources directly from conversations. Shows how to build a similar functionality for GCP.