Discover our curated collection of MCP servers for security & testing. Browse 2702 servers and find the perfect MCPs for your needs.
Enhances interaction between Claude and code by providing tools for code analysis, manipulation, and testing.
Enables security-focused large language model agents to natively access and utilize the urlDNA threat intelligence platform for threat detection and URL analysis.
Provides extensive network probing and diagnostic capabilities from multiple global vantage points.
Provides OPNsense API functionality through a Model Context Protocol (MCP) interface, enabling interactive network and system management.
Provides a secure, Docker-based environment for executing code snippets in multiple programming languages.
Demonstrates Model Context Protocol (MCP) servers and clients for AI-powered tools and multi-agent systems, facilitating code analysis, security scanning, and intelligent automation.
Analyzes SonicWall firewall logs from SonicOS 7.x and 8.x, providing intelligent log analysis, threat detection, and security insights via a Model Context Protocol (MCP) compliant interface.
Provides a specialized MCP server to assist AI assistants in thoroughly testing other MCP server implementations.
Enhances AI Agent penetration testing capabilities.
Enables large language models to manage users, threat models, and security data within the Devici platform via an MCP server.
Enables AI assistants to execute shell commands, read/write files, and explore the filesystem on your local machine.
Enables AI agents to debug .NET applications interactively by directly interfacing with the .NET runtime via the ICorDebug API.
Provides a read-only Model Context Protocol interface for Microsoft SQL Server, enabling secure metadata discovery and parameterized data retrieval.
Guides AI code generation to adhere to specific coding standards and architectural patterns, producing production-ready code.
Enforces the V-Model for Spec-Driven Development in Human-AI design, ensuring compliance and traceability for AI-generated code.
Exposes EMBA firmware analysis results as structured tools, enabling Large Language Models to query and reason about security findings.
Integrates Google's Threat Intelligence capabilities with AI assistants and custom applications via the Model Context Protocol.
Establishes a persistent, learning, and ethically governed identity for AI systems, enabling continuous interaction and growth across sessions.
Enhances AI agent reasoning with a 6-stage cognitive pipeline, multi-layer anti-hallucination, and robust bias detection.
Establishes a CI/CD quality gate for AI-generated code, proactively identifying issues such as hallucinated imports, stale APIs, and security anti-patterns.
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