"Understanding Frontend and Backend Development" matching MCP servers:

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 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.

MCP servers for real-time development monitoring and interactive pair programming.

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.

Enables AI assistants to connect to browser DevTools and backend debuggers for full-stack debugging, including frontend console, network, performance, and backend log analysis.

A 100% local development monitoring tool that captures browser console logs, network requests, and backend server output for analysis by AI assistants via MCP. It enables LLMs to debug applications by providing structured, real-time access to full-stack log data and persistent local storage.

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.

Integrates Sentry error monitoring with AI-powered analysis to automatically capture frontend JavaScript errors and provide intelligent repair suggestions through multiple AI models including OpenAI, Claude, and Gemini.

Read-only MCP server for izlek, a terminal-first local frontend observability tool. Enables agents to query live session status, logs, errors, and HTTP request/response records from a running development session.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

Unified observability gateway for AI agents — one MCP server for Prometheus, Loki, and any backend.

Enables AI assistants to manage remote servers via SSH with 14 commands for execution, file transfer, auditing, and monitoring.

AI development observability platform. It silently captures structured data from vibe coding sessions via MCP and codifies deviation patterns into project rules to make AI write better code. 100% local and zero runtime dependencies.

Enables interaction with Beszel system monitoring tool to query system and container statistics, manage alerts, and monitor infrastructure through its PocketBase backend.

Enables tracking and analyzing development time through the Wakapi API. Provides tools to retrieve coding statistics, project details, leaderboards, and recent activity logs for productivity insights.

Provides AI coding agents with real-time visibility into local development runtime state, enabling them to tail application logs, inspect ports, monitor process metrics, and diagnose network errors.

Integrates Honeybadger error tracking with Cursor IDE, allowing developers to fetch, analyze, and troubleshoot application errors directly from their development environment.

Enables monitoring and control of the SST development process by streaming dev logs, managing Lambda invocations, and executing lifecycle commands like start, stop, and restart. It provides real-time access to event streams and deployment status directly through the Model Context Protocol.

Enables querying OpenTelemetry logs stored in OpenSearch across development and production environments. Provides tools for searching logs by various criteria including free-text Lucene queries, trace IDs, service names, error levels, and specific fields.