"Penetration Testing" matching MCP servers:

Enables agents to store, search, and manage a personal penetration testing knowledge base via MCP, using BM25-ranked retrieval with Chinese tokenization, plus draft approval, deduplication, and sensitive-data redaction workflows.

MCP server exposing the OWASP Web Security Testing Guide via semantic search and knowledge graph traversal.

This MCP server enables intelligent API testing automation by combining RAG knowledge retrieval with tool execution capabilities. It allows QA engineers to perform natural language-driven API testing with contextual knowledge support.

Enables AI agents to perform offline cybersecurity research and penetration testing by querying a local knowledge base of curated security data, with tools for searching, answering, and status checking.

Provides AI assistants with real-time access to 1000+ AI models including their latest pricing, context windows, capabilities, and specifications. Supports model search, comparison, recommendations, and live testing.

A fake web search server that generates LLM-powered search results and full articles based on a configurable background story for AI alignment testing. It enables users to simulate a controlled web environment where AI agents interact with fabricated content tailored to a specific narrative.

Enables AI agents to conduct local, auditable opportunity discovery by collecting public discussions, tracing claims to exact quotes and source provenance, testing claims with counterevidence, and exporting deterministic evidence packs.

Integrates AI assistants with the Unity Editor for scene manipulation, asset management, and testing, plus Unity documentation search via RAG.

Enables AI agents to query a curated, cited knowledge graph on testing, benchmarking, and auditing autonomous agents, returning claims with sources, confidence values, and evidence tiers through eight read-only tools over a remote streamable-HTTP endpoint with no authentication required.

Enables interaction with Azure AI Foundry services through a unified interface for model exploration and deployment, knowledge indexing and search, AI evaluation, and fine-tuning operations. Supports both GitHub token-based model testing and full Azure deployment workflows.

Enables intelligent Kubernetes deployments, automated documentation testing, organizational pattern management, and shared prompt libraries. Provides AI-powered recommendations based on cluster capabilities and automates DevOps workflows through conversational interfaces.

Integrates the OpenViking context database with Cloudwise SA Workflow Skills to enable semantic search and access to product catalogs or solution templates. It supports hierarchical context loading for optimized token usage and includes a mock mode for testing without a live backend.

A Model Context Protocol server that provides tools for fetching xkcd comics and resources for dynamic data retrieval, WebSocket echo functionality, and static JSON data testing.

Provides FHIR Implementation Guide facts via MCP tools, enabling querying of must-support, bindings, constraints, and value-set usage from StructureDefinition artifacts for conformance and testing support.