"Information on Penetration Testing (Pentest)" 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.

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.

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.

AutoMem MCP provides persistent memory for AI assistants, enabling them to recall information across conversations and platforms with graph-vector retrieval.

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.

Enables AI assistants to maintain persistent project context across sessions by storing and retrieving structured information in markdown files organized in a memory bank directory.

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

This MCP server provides persistent memory integration for chat applications by utilizing a local knowledge graph to remember user information across interactions.

Enables Claude to maintain persistent memory across conversations using a local knowledge graph with fuzzy search capabilities, allowing it to remember and recall information about users, relationships, and context.

An MCP server that enhances sequential thinking with meta-cognitive capabilities including confidence tracking, hypothesis testing, and organized memory storage through graph-based libraries and structured JSON documents.

A custom Memory MCP Server that acts as a cache for Infrastructure-as-Code information, allowing users to store, summarize, and manage notes with a custom URI scheme and simple resource handling.

A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.

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 querying and retrieving content from Confluence through CQL searches and page content fetching, allowing Claude to seamlessly access information stored in Confluence workspaces.

A Model Context Protocol server that enables AI assistants like Cursor to directly query and retrieve information from Dify knowledge bases through natural language.

Browser automation MCP server with persistent memory for AI assistants, enabling automated web testing and workflow replay with self-healing selectors.

Enables seamless integration between Claude and Dry smartspaces, turning conversations into persistent, structured data by creating memories and querying information.

Provides agents with durable, provenance-aware memory through tools for remembering, recalling, answering, and maintaining information, while structurally resisting injection and confabulation.

Gives Claude Desktop, Cursor, Cline, and other MCP-compatible AI tools persistent memory, enabling them to store and recall information seamlessly across sessions.

Persistent AI memory server with hybrid search and embedded sync. Enables AI agents to store, retrieve, and manage information across sessions with temporal knowledge graph support.

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.

Provides a shared project knowledge and handoff layer for coding agents, offering tools to list, search, retrieve, and update stages of project information to maintain continuity across long-running projects

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