"Google Cloud Platform (GCP) resources and information" matching MCP servers:

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

Enables AI assistants to interact with and manage Google Cloud Platform resources including Compute Engine, Cloud Run, Storage, BigQuery, and other GCP services through a standardized MCP interface.

Enables AI assistants to interact with Google Cloud Platform services for log analysis and root cause investigation. Provides tools to query Cloud Logging, detect error patterns, and perform real-time log streaming across multiple GCP projects.

A read-only MCP server that enables AI agents to act as GCP platform engineers, allowing them to investigate incidents, take inventory, and find cost-optimization opportunities in Google Cloud projects without mutating any infrastructure.

Provides tools and context for interacting with Google Cloud services, enabling natural language management of billing, monitoring, logging, IAM, Spanner, and more.

Enables AI assistants to query, search, and analyze logs across Google Cloud Platform projects. It supports advanced filtering by severity or resource type and provides detailed log entry retrieval and project listing capabilities.

Read-only MCP server that exposes Kubernetes platform state (tenants, pods, SLOs, ArgoCD applications, chaos schedules, and catalog services) to AI agents, enabling natural language queries about cluster health and configuration.

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.

Lets your coding agent talk to the RunWhen platform — workspace chat, issues, SLXs, run sessions, and the Tool Builder — over the Model Context Protocol. Enables workspace chat with AI assistant, task authoring via Tool Builder, and direct data access to workspaces, issues, SLXs, run sessions, and more.

Inspect and manage IONOS CLOUD infrastructure via MCP

Enables MCP hosts like ChatGPT and Claude to query data platform metadata, explore schemas and tables, inspect SQL explain plans, and monitor DAG and ETL status through standardized tools.

Connects MCP clients to Gemini Cloud Assist APIs, enabling users to understand, manage, and troubleshoot their Google Cloud environment using natural language.

Enables local or HTTP-based MCP interactions with a mock SAP Cloud ALM environment, enforcing read/write capability separation and fail-closed security policies for future SAP Cloud ALM connectivity.

An MCP server for the Frappe Cloud control plane that lets agents manage sites, bench groups, servers, backups, deploys, logs, analytics, and billing. It provides read, write, and optional destructive tools for operating Frappe Cloud hosting infrastructure.

A framework for building MCP servers with built-in enterprise capabilities such as authentication, authorization, telemetry, audit logging, and data classification, allowing domain developers to focus on business logic while maintaining governance and compliance.

Enables AI agents and assistants to inspect live system telemetry, check TCP port connectivity, audit environment variables for leaked secrets, analyze and summarize log files, and list Docker containers — all with token-efficient, compressed output.

An MCP server that enables conversational interaction with Wazuh SIEM, allowing users to investigate alerts, hunt threats, tune false positives, edit rules, and run security actions via natural language.

Enables LLM clients to inspect and query your own GCP and AWS infrastructure, including BigQuery/Athena, CI build systems, container/serverless runtimes, and their logs through configurable environments.

An MCP server that enables interaction with Google Cloud Logging API, allowing users to write, read, and manage log entries and configurations through natural language.

Enables diagnosis of Google Cloud Platform logs using Gemini AI via MCP tools, fetching logs from Cloud Logging for issue analysis and root cause identification.

Enables AI agents to check live operational status, incidents, maintenance windows, and outage/reliability summaries for Iranian cloud providers, and to read and compare public prices for plans and resource configurations. It is read-only, requires no API key, and returns compact JSON for use in Claude, Cursor, Copilot, and other MCP clients.

Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.

An MCP server implementation for accessing Google Analytics 4 (GA4) data, built using the Model Context Protocol TypeScript SDK.

Enables AI agents to query Google Analytics 4 data, including listing accounts and properties, running historical and real-time reports with customizable metrics and dimensions.

Enables ingestion and full-text search of Windows application logs via MCP tools. Supports dual-backend indexing with Elasticsearch and SQLite for fast retrieval.

Enables natural language queries of the Chrome UX Report API to fetch Core Web Vitals (LCP, INP, CLS, FCP, TTFB), compare form factors, benchmark pages against site averages, and analyze up to 40 weeks of performance trends.

Enables AI assistants to manage Sciple platform content including environments, services, dashboards, runbooks, projects, and tickets through the Sciple REST API.

Enables authenticated clients to discover and call tools for scoped file listing/searching, request client-side LLM sampling, and receive real-time progress/log notifications over stdio or secure Streamable HTTP, with tenant/user workspace isolation and a loopback browser dashboard.

Enables LLMs to manage and monitor Hetzner Cloud infrastructure, including servers, firewalls, networks, and snapshots, while keeping the API token local and secure.

Enables autonomous AI agents to safely inspect, optimize, and manage live search campaigns through 38 purpose-built tools covering bids, budgets, keywords, RSAs, assets, and negative keyword harvesting. Bundles two-layer Pydantic execution shields with bounded bid ceilings and fixed stepping, a preflight gatekeeper, and W3C distributed tracing that separates upstream Google Ads API latency from local execution overhead.