"How to Test Kubernetes Network Policies" matching MCP servers:

An end-to-end test MCP server built with FastMCP that exposes a REST API as a set of tools for AI agents. It enables LLMs to perform CRUD operations on an upstream API by mapping HTTP methods to MCP tools.

A minimal MCP server with greet, add, and whoami tools, designed to validate deployment pipelines and environment variable injection.

Enables test execution and management through MCP clients, allowing retrieval of projects, listing tests, executing tests with browser selection, and monitoring results.

Facilitates unified execution and result parsing for various testing frameworks, including Bats, Pytest, Flutter, Jest, and Go, through a Model Context Protocol interface.

An MCP server that adds engineering discipline to AI-assisted development, enforcing evidence-gated TDD, security review, backup strategy, and deployment generation to turn AI-generated code into production-ready software.

A test harness for MCP servers that validates server JSON, tool schemas, performs golden file diffing and schema drift detection, and generates signed test reports with Shields.io badges.

Converts Jenkins declarative pipelines into GitHub Actions workflow YAML files, facilitating migration from Jenkins to GitHub Actions CI/CD.

A Model Context Protocol (MCP) server that provides Xcode-related tools for integration with AI assistants and other MCP clients.

Enables automatic generation of test scenarios from user stories uploaded to Claude desktop. Leverages MCP integration to streamline the test case creation process for development workflows.

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

Enables automated analysis of test suite traces to identify timing races and state pollution, helping detect flaky test non-determinism in CI/CD workflows such as Datadog CI or Trunk.

Publishes static sites to Drop2Run from an AI agent: hand it files written in the chat or a folder on disk, and it returns a live HTTPS URL. Six tools — publish_files, publish_dir, list_sites, delete_site, and login / login_code — with no git, no build step and no repository.

Enables MCP-capable agents to queue and evaluate real ComfyUI workflows through a live instance, returning structured pass/fail results without needing to know Playwright or ComfyUI internals.

Connects AI coding assistants to Gaffer test history and coverage data to analyze project health, debug failures, and identify untested code areas. It enables tools to track test stability, cluster failures by root cause, and monitor code coverage trends across projects.

Enables AI agents to audit Kubernetes manifests for configuration traps that pass linters but silently break production, via read-only tools to audit YAML, list known traps, and explain trap details.

Enables scanning Python repos for dead code and automatically drafting a branch + PR for the safe subset (unused single-name imports) after running the repo's full test suite. All other findings are reported without modification, and a human always merges.

Enables AI assistants to summarize Playwright test runs, identify flaky tests, heuristically triage failures, generate Playwright test stubs, and retrieve recent GitHub CI status through MCP tool calls.

Stops your AI from re-introducing bugs, leaking provider keys, or weakening tests. Bug fixes become permanent regression guards; blocked mistakes become AI lessons the agent reads and learns from before its next edit.

A Model Context Protocol server that enables AI assistants to interact with Azure DevOps services, allowing users to query work items with plans to support creating/updating items, managing pipelines, handling pull requests, and administering sprints and branch policies.

Compacts noisy test, build, and cloud-log output before an AI agent reads it — dedupes repeats, collapses stack frames, folds Playwright retries, and renders CloudWatch/GCP JSON logs down to the signal. Typically 80–95% fewer tokens on failures. Tool: compact_output. Run: npx -y logslim logslim-mcp