"Docker" matching MCP servers:

Connect to your Crawl4AI docker API server.

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

Enables AI clients to explore MCP concepts, retrieve quickstart guides, and ask questions with Redis-cached answers, supporting deployment on Cloudflare Workers and Docker.

A Python-based SQLite MCP server supporting database read/write operations with natural language interaction capabilities, packaged for Docker deployment in stdio mode.

A production-minded RAG service for MCP that answers questions over your documents with hybrid retrieval, PII redaction, and source citations, packaged for Docker/Kubernetes.

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.

Agent-agnostic persistent memory backend. 13 MCP tools, Supabase + Jina embeddings, multi-profile isolation, semantic recall across sessions.

Connects AI assistants to IT Glue, the MSP documentation platform. Browse organizations, read and write documents and flexible assets, and answer natural-language questions via semantic vector search (OpenAI/Azure embeddings). Viewer/editor/admin roles gate tool access; bring-your-own-key applies IT Glue's native permissions. Runs over stdio or HTTP; Docker image available.

Enables LLM agents to access a private knowledge base through MCP by automatically chunking and indexing .txt documents, with zero configuration and no Docker or vector database required.

Persistent memory for AI agents with knowledge graph, Hebbian learning, 4-signal RRF fusion search, GraphRAG enrichment, FSRS spaced repetition, and anti-hallucination grounding. 12 tools, PostgreSQL.

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.

MCP server that lets Claude analyze huge files (logs, dumps, etc.) via a sandboxed Python REPL and recursive language models, keeping content out of the prompt. Zero-setup with Claude Code login, Docker sandbox by default, and tools for loading, searching, chunking, and querying large contexts.

Enables LLMs to interact with Neo4j graph databases using natural language to execute Cypher queries and introspect database schemas. It supports both read and write operations for local, Docker, and cloud-based instances like Neo4j Aura.

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.

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.

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. Uses Ollama or OpenAI to generate embeddings. Docker files included

A Docker-based local RAG backend that provides advanced document search capabilities using vector, graph, and full-text retrieval via the Model Context Protocol. It supports over 28 file formats and tracks evolving relationships between concepts using a Neo4j-backed graphiti implementation.

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.

Integrates GitIngest to generate context files from public and private GitHub repositories, optimized for LLM consumption. Supports flexible file filtering, Docker deployment, and secure token-based authentication for private repository access.

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