"AWS cloud service monitoring" matching MCP servers:

Enables AI assistants to manage multi-cloud resources (AWS, Azure, GCP) including resource operations, cost analysis, monitoring metrics, and security compliance checks through natural language commands.

Connects AI assistants to AWS infrastructure through AWS IAM Identity Center for secure, natural language management of cloud resources across multiple accounts. It enables users to execute AWS CLI commands, monitor resources, and run remote shell commands on EC2 instances via Systems Manager.

Cloud security audit tools for AI agents that provide direct access to cloud APIs to read, correlate, and fix misconfigurations across AWS, Azure, and GCP.

Provides cloud architecture analysis, recommendations, and artifacts (diagrams, ADRs, deployment comparisons) for any project, supporting AWS, GCP, and Azure.

Cloud infrastructure provisioning via natural language. Enables deploying production-ready infrastructure on AWS, GCP, Azure, or Oracle with a single prompt.

Enables AI clients to assess infrastructure-as-code deployment risk, generate incident response runbooks, review CI/CD delivery readiness, and calculate SLO error budgets through structured advisory tools. Runs locally over stdio without cloud credentials or external API calls, returning guidance for platform work, pull request review, and production readiness checks.

Deploys and manages projects on Swigs Cloud from any MCP client, turning folders or git repositories into live HTTPS sites in about two minutes.

A small AWS MCP for AI assistants: one server, one config entry, SSO re-auth baked in, generic CRUD over hundreds of resource types, live docs lookup, server-side scripting for batched workflows.

Enables AI assistants to read-only inspect AWS IoT Core fleets, including things, device shadows, topic rules, certificates, fleet health, connectivity, and indexing configuration, without any write path.

Enables MCP clients to query AWS Billing and Cost Management data via local AWS CLI, including cost usage, forecasts, anomalies, budgets, and commitment utilization for analysis.

MCP server for the Partiri Cloud PaaS platform. Enables AI agents to manage workspaces, projects, services, deployments, and metrics.

Enables freelancers to create, store, render, and manage service agreements with scope, deliverables, rate, termination, liability, and signature blocks, including a before-send checklist and clause library.

Cross-cloud observability for AI agents. Discover resources, correlate logs, and diagnose infrastructure issues across AWS, GCP, Vercel, and Cloudflare — without leaving your editor.

Enables autonomous agents to segment audiences, manage subscribers, and trigger Journey Builder workflows in Salesforce Marketing Cloud through natural language.

A Model Context Protocol (MCP) server providing comprehensive cloud region data across all major providers. Query, filter, and compare cloud infrastructure locations worldwide.

Enables AI agents to discover, evaluate, and provision cloud infrastructure across AWS, GCP, and Azure with cross-cloud normalization, cost comparisons, and deployable execution kits.

Enables AI agents to price, deploy, pay for, monitor, update, and cancel applications on a decentralized cloud using a generated key pair, with plain-language specs converted into validated network specifications. Also provides container logs, live instance stats, and network or pricing guidance as read-only tools.

Gives MCP-compatible AI clients read-only access to fleet-wide backup monitoring across SIRIS, ALTO and Cloud SIRIS/DBMA appliances. Aggregated health checks and per-client rollups surface broken backups, stale snapshots, failed verifications and expiring service periods in a single tool call.

Enables Genesys Cloud queue administration and conversation analytics, including queue search/creation, conversation sampling, voice quality, sentiment, topics, transcripts, and OAuth client usage.

Enables analyzing AWS cloud costs through natural language queries, providing cost summaries, anomaly detection, idle resource identification, rightsizing recommendations, and tagging compliance via Claude.