"Information about Docker containerization technology" matching MCP servers:

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

Provides AI assistants with access to 102 curated UK Government technology standards, enabling context-aware recommendations for accessibility, APIs, security, cloud strategy, and more.

Enables scoped project discovery, deterministic rule and profile management, and durable automation with trigger/pause/resume/cancel and audit history for approved project contexts.

DocPull v6 is a local-first MCP server that turns public web sources into cited, versioned context packs for AI agents, RAG systems, and developer workflows. It can fetch and sync public static and server-rendered sources such as changelogs, standards, API references, OpenAPI specs, feeds, repo/package metadata, papers, filings, pricing pages, blogs, and other web sources.

Enables querying Kapa.ai's documentation and Q&A capabilities for any project enrolled with Kapa, with tools for asking questions and searching sources.

Enables AI assistants to search and ask questions about documents stored in AnythingLLM workspaces through MCP tools.

Enables AI assistants to search and retrieve transcripts from the Entra.Chat podcast about Microsoft Entra ID, with timestamped YouTube links and guest information.

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

A Model Context Protocol server that allows AI agents to perform WHOIS lookups, enabling users to directly ask the AI about domain availability, ownership, registration details, and other domain information.

Enables semantic search and contextual conversations with your Calibre ebook library using vector-based RAG technology. Supports project-based organization, multi-format book processing, and OCR capabilities for enhanced content extraction and retrieval.

Provides LLMs with specialized knowledge about fountain pen inks, enabling intelligent ink search, color matching, and recommendations through various tools.

A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.

Provides personalized Data Engineering learning updates by fetching recent news about DE concepts, patterns, and technologies, while tracking user knowledge to present only new information relevant to their learning journey.

A server that connects MCP clients (Claude, Cursor, Windsurf, etc.) to EventCatalog instances, allowing users to ask questions about their event-driven architecture and get insights about domains, services, and message schemas directly within their development tools.

A Model Context Protocol server that provides natural language access to Cortex API, enabling users to query information about their system's structure and make faster decisions about services, incidents, and team responsibilities.

Enables AI agents to query up-to-date, sourced information about workflow automation services, AI agents, integrations, pricing, case studies, FAQs, and contact details in Dutch and English through read-only tools.

Exposes a personalized AI agent that reads your resume and provides intelligent responses about your professional background through a standardized MCP server interface with RAG capabilities.

The Wanaku MCP Router stands between autonomous AI agents and your enterprise systems. Wanaku leverages proven integration technology, like Apache Camel, to set up and manage hundreds or thousands of integrations.

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.

Enables MCP clients to remember user preferences and behaviors across conversations using vector search technology.

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.

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.

Enables Codex agents to save, retrieve, and manage technology bookmarks as sourced, reviewable cards, with consent-based suggestions and trial planning.

Cortex MCP provides AI agents with long-term portfolio memory by transforming project history and developer decisions into a structured, queryable knowledge graph. It enables assistants to maintain context across different repositories by exposing historical patterns, architectural decisions, and technology preferences through the Model Context Protocol.

Enables agents to answer questions about Thailand's Personal Data Protection Act by combining hybrid dense/sparse vector retrieval with knowledge-graph traversal over a 190-node graph of the statute's 96 sections. Exposes tools for hybrid section search, related-section lookup, penalty tracing from a violated section, deterministic full-text retrieval, and graph schema summaries, all running locally via Docker.

Enables MCP clients like Cursor and Claude to remember user preferences and behaviors across conversations using vector search technology.

Connect to your Crawl4AI docker API server.

Enables Claude to intelligently query and analyze your resume using RAG technology. Supports skill matching against job requirements and answering questions about your professional background from locally stored resume files.

MCP server for local-first RAG over Obsidian vaults, enabling AI agents to search and ask questions about notes with grounded citations.

CTX is a tool made to solve a big problem when chatting with LLMs like ChatGPT or Claude: giving them enough context about your project.

Enables users to query information about LPDP scholarship financial disbursement using RAG with Pinecone vector search and Gemini 2.0 Flash, answering questions about funding components, deadlines, living allowances, and required documents.

A Model Context Protocol (MCP) server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.

An MCP server that provides deep knowledge about OpenAI APIs and SDKs, enabling users to query technical information through various MCP clients including ChatGPT Deep Research, Cursor, and OpenAI Responses API.

Provides AI agents with complete web search, crawling, and RAG capabilities through a Docker-based solution combining Model Context Protocol, Crawl4AI, SearXNG, and Supabase.

A persistent memory system using a local knowledge graph that enables Claude to remember information about users across chats, with advanced search, graph traversal, and filtering capabilities for entities, relations, and observations.

A unified Docker container that runs Qdrant vector database and provides REST API and MCP interfaces for vector storage and semantic search, compatible with Claude vector hooks.

Enables local-first, source-cited AI answers about your TTRPG campaign lore from your own documents, usable in Discord, Foundry VTT, or any MCP client while refusing to invent information outside your sources.

A local-first iMessage retrieval system that serves hybrid full-text and vector search over an MCP server, enabling an AI assistant to answer questions about your correspondence.

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

A fully self-hosted MCP server that integrates the Mem0 framework to provide persistent memory capabilities for AI assistants using local models and vector storage. It enables users to store, search, and manage contextual information across conversations through a Docker-based deployment.