"Building and Deploying a React Application with Docker and a Postgres Backend" matching MCP servers:

Enables AI assistants to explore a PostgreSQL database, run bounded read-only SQL, inspect query plans, test hypothetical indexes, find slow queries, and check database health with compact CSV output.

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.

More than just an MCP server that covers the entire range of Docker management tasks, it helps you create workflows to easily manage your Docker environments at scale. It allows you manage multiple local or remote (TCP, TLS, SSH) docker instances and exposes things like logs and stats as resources so that you can monitor and triage all of them.

A Postgres MCP server with index tuning, explain plans, health checks, and safe sql execution.

Connects AI agents to a running React Native app so they can see the screen, tap and type, and inspect logs, network traffic, and state without any SDK or code changes. Composite tools let an agent run a whole interaction flow in a single call instead of one round trip per tap.

An MCP server that connects to your React Native application debugger

Provides AI agents with visibility into React applications by exposing tools to inspect component state, props, and performance metrics. It enables debugging and state analysis for both web and React Native applications through the Model Context Protocol.

A comprehensive MCP toolkit for Java backend developers, providing 35 tools across 5 servers for database analysis, JVM diagnostics, migration assistance, Spring Boot monitoring, and Redis diagnostics.

Enables AI agents to manage Docker containers through a secure MCP interface, supporting container lifecycle operations, log inspection, resource monitoring, and system diagnostics with role-based access control.

Streams live React profiler data to AI agents, enabling them to analyze performance and suggest specific, actionable fixes for slow components.

A read-only MCP server that exposes your Docker daemon, allowing AI assistants to list containers, inspect their details, and fetch logs without write access.

Enables natural-language management of Docker containers, images, volumes, and networks, with autonomous diagnosis and remediation of container failures through a multi-agent system.

Enables AI agents to analyze, optimize, and safely interact with PostgreSQL databases, including health checks, index recommendations, query planning, and SQL execution with read-only and production protection modes.

A Postgres MCP server with index tuning, explain plans, health checks, and safe SQL execution.

Governed PostgreSQL DBA operations — slow-query, bloat, and blocking-lock RCA, index management, vacuum/analyze, and replication lag, with unbypassable audit logging (MCP + CLI), budget/runaway guards, dry-run, and undo/rollback.

MCP server for PostgreSQL that enables schema exploration, safe SQL execution, EXPLAIN analysis, health checks, and index recommendations across multiple databases.

A Model Context Protocol server that provides secure database access to PostgreSQL through Kysely ORM, enabling natural language interaction with PostgreSQL databases. It offers 40+ MCP tools for querying, schema inspection, diagnostics, and Java backend monitoring.

Enables AI agents to interact with PostgreSQL databases through schema intelligence, query execution, and DBA tooling including index analysis and health monitoring. Features configurable access levels and audit logging for secure database operations.

Enables local LLM agents to inspect Docker containers and optionally perform guarded restarts through MCP, with read-only defaults, credential redaction, canonical ID checks, denylists, and bounded daemon timeouts. It provides secure, zero-trust Docker daemon access for MCP clients.

Enables AI assistants to inspect and manage Docker environments across one or multiple hosts, including listing containers, retrieving logs and stats, and starting, stopping, restarting, or removing containers, with read-only and secret-redaction safeguards.

Read-only MCP server for one Docker host: list containers, inspect (without env vars), logs with time windows and search, stats and events. Cannot start, stop, remove or exec.

Enables AI agents to inspect PostgreSQL health, tune indexes, review and optimize EXPLAIN plans, understand schemas for context-aware SQL, and execute SQL safely with read-only/restricted or unrestricted access modes.

Effortlessly orchestrate and oversee Docker environments from anywhere, using natural, conversational commands.

Bridges React Native DevTools, enabling AI assistants to debug, profile, and inspect React Native applications through a standardized protocol.

Enables AI agents such as Cursor and Claude Desktop to read a running debugger's captured console logs and network sessions, inspect full request/response payloads, diagnose crashes and failed API calls, and trigger React Native reloads or the developer menu.

Enables named, authenticated callers to run read-only introspection of a Google Cloud project and to read and write per-tenant notes that stay isolated between callers. Every request is scope-checked against a reviewed policy, rate-limited per caller, and audited without ever logging argument values.

An open-source MCP server for PostgreSQL that provides database health analysis, index tuning, query plan optimization, and safe SQL execution, suitable for both development and production environments.

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.

Enables MCP clients to retrieve lab performance scores and field Core Web Vitals data for web pages via Google APIs.

Enables AI assistants to manage, monitor, and optimize PostgreSQL databases with over 200 specialized tools for operations, security, performance tuning, and diagnostics.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

An MCP server that gives any LLM client the ability to list, inspect, start, stop, and monitor Docker containers on the host machine.

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

Checks MCP servers by URL, npm package, or official MCP Registry search, reporting availability, response speed, exposed tools, resources, and prompts. It flags changes since prior runs as breaking, warning, or info using saved snapshots.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

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 local computer diagnostics by sampling CPU, memory, disk, and network activity and displaying the busiest processes in an interactive dashboard. It also supports human-confirmed process-stop proposals while keeping diagnostics local and bounded.

Enables automated review and analysis of network security controls (AWS Security Groups, Network ACLs) using natural language queries. Supports NSC configuration reviews, network segmentation testing, and identifying security gaps through conversational LLM interaction.

A comprehensive MCP server providing advanced Redmine project analytics, web automation via Playwright, and iTunes music integration. It enables detailed tracking of sprint metrics, bug counts, and team performance through a token-optimized tool architecture.

Enables AI agents and users to manage workspace files, monitor system metrics, take persistent notes, and retrieve weather data via MCP tools and resources.