"Information on Penetration Testing (Pentest)" matching MCP servers:

Enables MCP-compatible agents to query structured penetration testing engagement state captured from tmux sessions, including credentials, hosts, and command provenance with exact source bytes.

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.

On Board is a local MCP server that gives multiple AI agents and IDEs a shared project memory, ticket queue, and handoff history, so agents can seamlessly continue each other's work. It supports agent-to-agent wake events, enabling autonomous workflows like reject-fix-resubmit cycles without human relay.

A server that manages conversation context for LLM interactions, storing recent prompts and providing relevant context for each user via REST API endpoints.

Structures debugging processes into a persistent knowledge graph, enabling problem decomposition, hypothesis testing, and reusable solution discovery.

An MCP server for managing agent memory using provenance tracking, decay-weighted retrieval, and feedback loops to optimize information recall. It allows agents to store insights in a local SQLite database and rank them based on confidence, age, and usefulness.

Provides persistent, searchable memory for AI agents, enabling them to retain, recall, and reflect on information across conversations.

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.

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.

MCP server for querying the DodaOne governance framework, enabling users to retrieve governance information and evaluate proposed actions.

A persistent memory layer for AI tools that decouples personal data from AI's unstable memory, enabling you to mention information once and have it remembered forever across all conversations.

Enables MCP clients to remember user information, preferences, and behaviors across conversations using vector search technology. Built on Cloudflare infrastructure with persistent storage and semantic similarity matching.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.

An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.

Enables AI assistants to remember user information and preferences across conversations using vector search technology. Built on Cloudflare infrastructure with isolated user namespaces for secure, persistent memory storage.

Provides a single-user, append-only, queryable memory store for dated facts with provenance, enabling Claude to persist and correct personal information across conversations via an MCP interface.

A personal second brain on Cloudflare Workers that stores knowledge as a semantic graph with auto-linking, deduplication, and MCP tools for AI agents to save, search, and connect information.

Read-only MCP server for retrieving Muninn's memory via a single recall tool, with parity testing against live Python outputs.

Enables storing and retrieving information using semantic search with Qdrant vector database. Acts as a memory layer for LLMs to persistently store and semantically search through information and metadata.