"Testing Kubernetes Network Policies" matching MCP servers:

Exposes a Kubernetes cluster to MCP-compatible AI clients, enabling read-only and optional write operations on cluster resources like pods, deployments, and namespaces through natural language.

Provides echo tools for testing MCP protocol functionality with message echoing, delayed responses, and JSON data analysis capabilities.

Turns Claude into a penetration testing copilot with 80 security tools, safety controls, and automatic reporting.

Exposes network-monitoring tools (query metrics, analyze windows, compare, logs, status, runbooks, speed tests) as an MCP server for agentic workflows. Designed with evaluation suites, cost-aware model routing, and semantic tool retrieval.

Provides policy-based access control, incident tracking, and compliance monitoring to govern AI agent behavior. It enables organizations to enforce security rules and maintain audit trails by validating agent actions against trust levels and pattern-based policies.

Enables comprehensive threat analysis for Indicators of Compromise (IoCs) including IP addresses, file hashes, domains, and URLs. It provides detailed reputation scores, security vendor evaluations, and network metadata to facilitate security assessments and risk detection.

Provides 170+ tools to manage UniFi networks via the internal controller API, enabling AI assistants to perform full network management including clients, devices, WLANs, firewall, and more.

A Model Context Protocol server framework featuring dynamic tool loading and automatic tool discovery from a dedicated directory. It leverages FastMCP to provide a robust environment for building, configuring, and testing individual Python-based tools.

A template and demonstration project for building, testing, and deploying remote MCP servers using FastMCP and uv. It provides a foundational structure for creating MCP-compliant tools that can be hosted publicly and integrated with LLM agents.

A Model Context Protocol server built with FastMCP that enables dynamic tool loading and configuration from individual Python files. It provides a flexible framework for automatically discovering, testing, and running tools via Stdio or HTTP transport modes.

Enables financial data retrieval, company context, market information, financial modelling, and valuation through MCP tools, with a deterministic sample provider for testing before connecting live data sources.

Enables complete Vultr cloud infrastructure management through natural language commands, providing 335+ tools across 27 services including compute, DNS, Kubernetes, databases, and networking. Features smart identifier resolution allowing users to reference resources by human-readable names instead of UUIDs.

Enables AI agents to control GNS3 network emulation labs. Supports building topologies, managing devices, capturing packets, and automating device CLIs.