"Optimizing and Improving Flutter Application Performance" 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.

Analyzes websites like a senior performance engineer and produces prioritized Core Web Vitals recommendations. Provides 16 tools for detailed performance analysis, including Lighthouse, CrUX, WordPress and Cloudflare inspections.

Enables web performance analysis using Google PageSpeed Insights and Chrome UX Report APIs, providing compact metrics and diagnostics without local browser automation.

Enables comprehensive analysis of AWS Lambda performance including cold start tracking, memory utilization monitoring, cost analysis, and optimization recommendations. Provides real-time metrics, comparative analysis across multiple functions, and actionable insights to improve Lambda performance and reduce costs.

A comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.

Monitors and analyzes mobile application performance data to detect severe issues such as excessive memory usage and view count growth. Provides intelligent analysis with customizable rules and integrates seamlessly with development workflows.

Collects page performance metrics using Playwright, CDP, and Lighthouse, and returns a normalized PerformanceReport.

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 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.

Provides read-only visibility into Snowflake warehouse cost and query performance, including credit usage by warehouse, expensive queries, and heuristic right-sizing recommendations.

Connect AI agents like Claude, Cursor, and Gemini to historical Windows performance and privacy data gathered by the AppControl Windows app. This server enables natural language queries into CPU, GPU, and temperature history alongside system security events for deep local auditing.

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.

Real-time Core Web Vitals (LCP) telemetry, DOM bloat analysis, and AI search crawler (GEO) readiness audits for AI coding assistants.

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.

MCP server that exposes Symfony profiler runtime data (requests, queries, logs) to AI agents like Claude Code for debugging and optimization.

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.

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 MCP clients and AI agents to inspect a running FLECS application through its built-in REST API, including querying entities, reading components, explaining queries, and inspecting systems and performance statistics. With explicit opt-in, it also allows small reversible changes to the live ECS world.

An MCP server that connects AI agents to Scouter APM, enabling natural-language queries against real-time application performance data.

Enables AI agents to access New Relic logs and APM data through the NerdGraph API. It allows users to execute NRQL queries, retrieve application performance metrics, and analyze transaction traces using natural language.