"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.

Monitor failing GitHub Actions workflows, auto-fix them with OpenCode, and retry the run — driven by a workflow_run webhook, coordinated through an MCP server.

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.

Enables AI coding agents to see their Odoo.sh projects by listing projects, branches and builds, waiting for a build to finish, reading build and server logs, and optionally rebuilding branches or updating the firewall. It runs read-only by default with bounded, time-limited tool calls, keeping the user's Odoo.sh session out of the agent's reach.

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.

Uptime monitoring your AI agent can set up: create HTTP, SSL, ping, TCP and DNS monitors checked from multiple regions, run checks on demand, read and acknowledge incidents, manage cron heartbeats, maintenance windows and status page updates.

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

Turns JUnit XML test reports from CI into queryable QA metrics, letting AI assistants answer plain-English questions about failures, flaky tests, regressions pinned to a commit, root-cause clusters, and performance drift. Also generates ready-to-share QA health reports and self-contained HTML dashboards without logging into separate tools.

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.

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.

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.

Enables Flutter developers on Windows or Linux to build, sign, test, and ship iOS apps without a Mac, using GitHub Actions macOS runners, App Store Connect API, remote Macs, and USB-connected iPhones.

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

An MCP server that enables AI agents to inspect projects, generate packaging contracts, build and verify Linux packages, generate multi-platform CI workflows, and orchestrate Windows/macOS/HarmonyOS builds with signing and real install/run validation.

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.

Enables assessing database change risk, estimating impact, generating rollback plans, prechecking change windows, and orchestrating dry-run, canary, apply, and rollback with approval gates and auditable state transitions.

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.

Enables AI assistants to interact with Woodpecker CI to debug pipeline failures, analyze build logs, troubleshoot configurations, and perform pipeline and repository actions such as triggering, restarting, canceling, approving, activating, and updating settings.

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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.

Enables evidence-first release readiness assessment by running or accepting build, API, browser, visual, performance, and security evidence, then returning SHIP, REVIEW, or HOLD recommendations with clustered regressions.

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.