"Windows Performance Toolkit and xperf.exe" matching MCP servers:

Converts non-functional requirements and code into secure, traceable JMeter performance tests with coverage analysis, plan generation, execution, and GitHub draft PRs.

provides AI-powered PostgreSQL performance tuning capabilities.

Automates 85-95% of the Secure Software Development Lifecycle (SSDLC) planning phase through multi-role AI orchestration, enabling business analysis, threat modeling, test strategy design, and security code review.

Enables orchestrating secure software development pipelines with domain-specific compliance (HIPAA, PCI-DSS, etc.), generating pseudocode, threat models, and CI/CD from user stories via natural language.

Enables intelligent Kubernetes deployments, automated documentation testing, organizational pattern management, and shared prompt libraries. Provides AI-powered recommendations based on cluster capabilities and automates DevOps workflows through conversational interfaces.

Bridges AI IDEs with Tencent CloudBase for seamless deployment, enabling users to go from AI-generated code to live applications with automatic cloud resource configuration, database setup, and intelligent debugging.

MCP server that analyzes GitHub Actions workflow performance, audits configuration for optimization, and provides billing and cache usage insights.

An MCP server that unpacks and structures Playwright trace.zip archives so AI agents can perform root-cause analysis on CI failures, with 16 focused tools for inspection, DOM/UI analysis, root-cause analysis, and performance analysis.

Enables building and cleaning Delphi projects (.dproj/.groupproj) on Windows using MSBuild with RAD Studio environment initialization. Supports both individual projects and group projects with configurable build configurations and platforms.

A specialized MCP server for QA engineering. It provides tools for user story analysis, test strategy, BDD/Gherkin scenarios, contract testing, integration tests, performance plans, security checklists, and CI/CD pipeline generation, accessible from any MCP-compatible IDE or client.

Um servidor MCP completo para integração com Sentry no Cursor, oferecendo 27 ferramentas para monitoramento de erros, performance e saúde de aplicações.

Karpenter is a Kubernetes Node Autoscaler built for flexibility, performance, and simplicity.

A production-ready template for building Model Context Protocol (MCP) servers in Python, using Docker Compose for containerized development and CI/CD.

Query Prometheus from CLI or any MCP client, with retries, caching, and an optional metrics exporter.

A boilerplate for building FastMCP servers, providing a basic setup for testing, linting, formatting, and publishing to NPM.

Provides Kubernetes deployment intelligence with typed query tools and AI-synthesized risk briefs, enabling users to list workloads, get detailed snapshots, query Prometheus metrics, record deployment history, and generate risk assessments before promoting to production.

An open source MCP server empowering SREs with intelligent observability, predictive analytics, and AI-driven automation across Kubernetes, OpenShift, and Tekton environments.

Interact with OneDev for CI/CD spec editing, pull request review, and issue workflow automation etc.

A Model Context Protocol server implementation that allows AI models to interact with and manage Spinnaker deployments, pipelines, and applications through a standardized interface.

A production-ready template for building Model Context Protocol servers in Python, featuring example tools, structured logging, and multiple transport protocols.