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

Multi-agent swarm orchestrator for AI workflows. Exposes blackboard read/write, agent dispatch, permission gating (AuthGuardian), token budget enforcement (FederatedBudget), and audit log tools over MCP. Supports 14 AI framework adapters including LangChain, AutoGen, CrewAI, Codex, and A2A — mix any frameworks in one swarm with race-condition-safe shared state.(Traffic light for agents)

Enables LLM agents to triage network incidents by fetching device telemetry, inspecting syslogs, and executing traffic reroutes through MCP tools.

Enables network discovery, port scanning, and infrastructure monitoring with device detection, service fingerprinting, and cluster health checks for autonomous AI systems.

Global mailbox and address book for AI agents, enabling asynchronous messaging across machines without requiring simultaneous online presence.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

A Model Context Protocol server that converts Markdown content to HTML format.

A server that enables LLMs to manage Kubernetes clusters through natural language commands, wrapping kubectl operations to provide a simplified interface for common Kubernetes tasks.

Enables MCP clients to interact with A2A agents through four ordinary tools, translating agent discovery, messaging, task reading, and cancellation between the A2A and MCP protocols, with support for the tasks extension.

An MCP server that turns a one-sentence idea into working, tested code using a pipeline of six AI agents, with optional deployment.

Enables Claude Code sessions to discover each other as named peers and exchange instant messages across directories, machines, and Docker containers, with durable delivery for offline sessions.

▎ Enables AI assistants to unleash a crowd of role-played virtual users — impatient shoppers, seniors, keyboard-only users, privacy hawks, chaos monkeys — on a website. Each persona browses in a real Chromium browser, files UX/QA findings in character, and the site receives a damage report with an S–F survival grade. Accusations can be cross-examined by up to three independent verification engines

A simple MCP server combining FastMCP and LangGraph that enables AI agent interactions through tools for asking questions to an OpenAI-powered agent and receiving personalized greetings.

Enables AI assistants to securely access Okta Identity Governance APIs for managing users, groups, apps, access requests, and certifications with configurable deployment modes and safety controls.

Exposes AI agents as MCP tools to fetch Jira stories, generate BRDs and Playwright test scripts, run tests, and auto-heal broken locators. Integrates with Claude Desktop and Cursor IDE for enterprise-grade test automation.

Public browse-first MCP server for AI capability discovery and grounded Agent feedback. The knowledge graph is read-only and ordinary visits do not trigger search or automatic posting.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.

An autonomous MCP server that uses the Claude Code CLI to solve coding problems without permission prompts.

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

An MCP server integration that enables Cursor AI to communicate with Figma, allowing users to read designs and modify them programmatically through natural language commands.

Enables users to control the cursor in Figma through verbal commands using an agentic AI agent, streamlining the design process with a new interaction method.