Discover our curated collection of MCP servers for security & testing. Browse 1772servers and find the perfect MCPs for your needs.
Enables AI agents to interact with tools and services through secure, sandboxed TypeScript code execution, replacing traditional sequential tool calling.
Enables AI assistants and LLM applications to securely execute code snippets within isolated containerized environments.
Manages, proxies, and secures Model Context Protocol (MCP) servers to control LLM activity.
Enables AI systems to interact with and instrument mobile and desktop applications through dynamic analysis.
Enables coding agents to write, run, and maintain integration tests for various data resources, APIs, and customer journeys.
Executes Python code in a secure, sandboxed environment using an MCP server.
Enables AI assistants to interact directly with IDA Pro for binary analysis tasks.
Streamline mobile app testing and automation for Android and iOS using AI-powered interactions and test generation.
Connects the Wazuh SIEM to applications needing contextual security data via the Model Context Protocol (MCP).
Executes whitelisted shell commands remotely with standard input support.
Enables secure execution of command-line operations with customizable security policies.
Enables Large Language Models to analyze Active Directory and Azure Active Directory environments through natural language queries.
Evaluates the robustness of AI assistant systems against common attack patterns, ensuring security and compliance.
Enables AI agents and applications to securely interact with the Binance Smart Chain for token transfers, DEX operations, and comprehensive wallet management.
Facilitates communication between JEB Pro and other applications through an MCP server.
Simulates HTTP traffic to benchmark and analyze web service performance.
Executes arbitrary JavaScript code in isolated, ephemeral Docker containers with on-the-fly npm dependency installation.
Integrates radare2 with AI assistants through a MCP server for binary analysis.
Enables interaction with CodeQL through structured commands for tools and AI agents.
Orchestrates multiple MCP tools on-demand, drastically reducing token usage for AI agent contexts.
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