model-context-protocol Tools

AiSOC is an open-source AI Security Operations Center platform that makes AI-driven security investigations transparent and auditable. It solves the problem of 'black-box' AI security tools by recording every agent step—prompts, tool calls, and reasoning—in a replayable investigation ledger, enabling compliance with SOC 2, ISO 27001, and DORA. It's built for security teams in regulated environments who need the power of AI agents but require full auditability and the ability to self-host.

Rea is a platform that uses AI agents to reverse engineer software, from high-level application behavior down to low-level native binaries. It solves the problem of understanding complex, undocumented, or legacy systems by automating the analysis process. This tool is designed for security researchers, software engineers, and developers who need to comprehend, audit, or modernize existing codebases.

codebase-memory-mcp is an MCP server that indexes repositories into a persistent knowledge graph of functions, classes, and dependencies. It solves the problem of AI agents inefficiently reading files one-by-one, enabling structural queries with ~120x fewer tokens. It's for developers and teams using AI coding assistants like Claude Code who need fast, local, and deep codebase understanding.

holaOS is an open-source, all-in-one workspace for AI agents. It solves the problem of fragmented AI tools by letting you run any agent (like Claude Code or Codex) across your entire digital environment—100+ integrations, apps, browser, and files—with shared memory for seamless context. It's built for developers and teams who want to centralize their AI workflows, offering built-in models or the flexibility to bring your own keys.

ADR is a production-proven system for securing enterprise AI agents built on the Model Context Protocol (MCP). It solves the critical challenges of limited observability, insufficient robustness, and high detection costs by providing comprehensive telemetry, systematic red teaming, and scalable, two-tier detection. It is designed for large enterprises deploying AI agents at scale, as demonstrated by its deployment at Uber.