"Building a Flutter and Go Application with PocketBase Backend Deployed on Oracle Cloud" matching MCP servers:

Connects AI coding clients to a running Flutter app's Dart VM Service so they can stream live exceptions with stack traces, logs, network calls, frame timings, memory, rebuild attribution and navigation state as structured tool results. It also correlates that evidence into root-cause diagnoses with confidence and suggested fixes, and streams everything to a browser dashboard.

Provides secure, multi-user access to Oracle Database for MCP clients, enabling SQL querying, execution, schema exploration, and performance analysis through various connection methods including TNS, JDBC, and TCPS.

Inspect and manage IONOS CLOUD infrastructure via MCP

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.

A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.

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.

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

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.

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

Enables AI assistants to monitor and troubleshoot AWS Application Signals services by tracking service health, analyzing SLO compliance, querying CloudWatch metrics, and investigating issues using distributed tracing with AWS X-Ray.

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.

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.

Provides tools for accessing Alibaba Cloud observability products including SLS (Log Service) and ARMS (Application Real-time Monitoring Service), allowing any MCP-compatible AI assistants to quickly interact with these services.

DCL Trust Oracle is a deterministic AI audit layer, natively integrated with MCP, that evaluates LLM and agent outputs against configurable policies before and after action. Every verdict is written to a tamper-evident, hash-chained audit log storing only cryptographic metadata — never raw content — enabling privacy-first forensic review.

Enables real-time monitoring of F5 Cloud service status, including 148+ service components, active incidents, and scheduled maintenance windows with intelligent caching and dual data source reliability.

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

Enables an AI assistant to diagnose a broken application on a local Kubernetes cluster by inspecting pods, events, and logs, with server-side guardrails, least-privilege RBAC, and an audit log. It can also restart deployments in allowed namespaces only after a dry-run preview and host approval.

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

MCP server that diagnoses ML model regressions by correlating drift reports, eval runs, and deploy logs, providing evidence-cited incident reports through a set of investigation tools.