"All About Docker" matching MCP servers:

MCP server exposing memory search, index, and stats tools for agents, with honesty guards to prevent re-litigation of settled decisions.

Self-hosted knowledge backend for AI agents. Provides 11 MCP tools for hybrid vector + keyword search, container-isolated knowledge bases, and 4 storage connectors (S3, Azure Blob, MinIO, filesystem). Built with .NET, runs via Docker.

Enables AI agents to parse PDFs and search local document context through multimodal extraction and vector-based retrieval, all exposed as MCP tools.

Memory for facts that change. A retrieved record is marked SUPERSEDED, with when a later record replaced it, so an assistant does not answer with a value that is no longer true. Single file, numpy the only dependency, runs with a local embedding model or none at all.

Enables Claude Desktop and compatible agents to search, read, add, update, and delete a locally stored, user-curated memory using hybrid retrieval with reranking and Qdrant. All processing runs locally without cloud services or API keys.

Enables asking natural language questions about local videos, using Whisper transcription, ChromaDB vector storage, and Groq for timestamped answers via MCP.

An experimental MCP server that makes YouTube playlist transcripts available for AI assistants, enabling search and conversation about video content.

Enables Claude to store and query personal finance transactions using semantic search. Transactions are persisted in ChromaDB and JSON, allowing natural language questions about spending trends and portfolio allocations.

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.

Local offline semantic search over documents (txt, md, pdf, docx, pptx, csv). Indexes folders into a LanceDB vector database with multilingual embeddings and supports hybrid vector + keyword search via Reciprocal Rank Fusion. No API keys, no cloud, no Docker required.

A self-hosted second brain MCP server that enables capturing thoughts with deduplication and semantic search using local embeddings and PostgreSQL with pgvector, all running on your own hardware.

Local-first semantic memory layer for MCP agents. Recall, remember, forget, stats over stdio. ChromaDB plus sentence-transformers, all on-machine.

Enables semantic search across Apple Mail, Messages, Calendar, and Contacts on macOS using natural language queries. All processing happens locally with privacy-first vector indexing for fast similarity search.

txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. All functionality can be served via it's API and the API supports MCP. Docs: https://neuml.github.io/txtai/api/mcp/

Enables AI assistants to semantically search your entire local conversation history from Claude Desktop, ChatGPT and Claude Code, and to retrieve, browse, ingest and report on those conversations. All embeddings run locally, so no cloud, API keys, or data leave your machine.

Enables AI agents to maintain persistent, multi-layer cognitive memory with vector search, deterministic verification, experiment tracking, and cost-aware recall, all through 87 MCP tools and a local Flask UI.

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.

Local podcast transcription, embedding, and semantic search with an MCP server for AI assistant integration. All processing runs on your machine.

A local AI memory system that stores all conversations verbatim and organizes them into navigable structures. It provides 19 MCP tools for AI assistants to search and retrieve past decisions, debugging sessions, and architecture debates automatically.

A native, local-first, multilingual memory server for Codex, Claude Code, and any MCP client, providing hybrid semantic and exact memory recall using Go, Qdrant, and bge-m3. It enables stable, concurrent memory storage and retrieval without Docker or cloud dependencies.