"World Health Organization" matching MCP servers:

Enables Claude Desktop to search, add, list, retrieve, reinforce, and delete memories through a remote HTTP memory API, with hierarchical sector classification such as episodic, semantic, and procedural. It also supports connectivity health checks and optional API key authentication for the backing graph and vector store.

MCP bridge to a multimodal RAG service, enabling hybrid search and Q&A over documents with tools for knowledge base queries and health checks.

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.

Enables MCP clients like Claude, Cursor, and Windsurf to search a local document knowledge base using four-layer retrieval fusion (vector similarity, BM25 keywords, knowledge graph traversal, and metadata filtering) with cross-encoder reranking. Also lets agents index documents, add single documents, list indexed sources, and run health diagnostics, with models kept warm between queries so no GPU, PyTorch, or API keys are needed.

Enables AI assistants to save, retrieve, and manage research content using ChromaDB vector storage with semantic search, topic organization, and automatic deduplication powered by OpenAI embeddings.

An MCP server that provides persistent semantic memory backed by PostgreSQL and pgvector for storing and searching thoughts via vector embeddings. It enables dimensional organization, conflict detection, and historical tracking of facts, decisions, and observations.

A secure vector-based memory server that provides persistent semantic memory for AI assistants using sqlite-vec and sentence-transformers. It enables semantic search and organization of coding experiences, solutions, and knowledge with features like auto-cleanup and deduplication.

Enables vector search and document retrieval, text extraction and chunking of files into Markdown, and private deep research generation through Vectorize pipelines. It connects MCP clients to a user's configured Vectorize organization and pipeline for retrieval-augmented workflows.

Enables Claude and other AI agents to store and retrieve persistent cross-session memories, so decisions, patterns, and knowledge survive context limits, restarts, and compaction. Supports self-hosted Redis/Valkey or managed cloud deployment with workspace organization and semantic search.

An MCP server providing semantic memory storage and retrieval using vector embeddings powered by LanceDB and Google Gemini. It supports multi-tenant isolation and bucket-based organization for managing structured memories through natural language queries.

An MCP server that integrates Turso databases with LLMs, supporting organization and database-level operations with two-level authentication.

Provides AI assistants with persistent graph-based memory capabilities using Neo4j, enabling semantic search, relationship tracking, and knowledge organization across multiple project contexts.

A universal database gateway implementing the Model Context Protocol, enabling MCP-compatible clients to connect, explore, and manage multiple databases with advanced features like OAuth2 authentication, health checks, and SQL optimization.

A production-grade MCP server and client implementation with comprehensive features including structured logging, health checks, metrics, authentication, and RAG capabilities with PostgreSQL vector search. Supports both stdio and SSE transports with containerization and security features for enterprise deployment.

Gives Claude Code read-only semantic search over an Obsidian vault by reading the embedding files the Smart Connections plugin already produces, so no API key or network is needed at query time and it works across the WSL/Windows boundary. Exposes tools for meaning-based note and block search with folder scoping, related-note discovery, note reading, and index health checks.

A personal knowledge MCP server with vector database for the Opencode ecosystem. It enables semantic search, tag organization, and session memory logging, all running locally without external API keys.

Allows LLMs to access, scan, and search photos on connected iOS devices via USB using libimobiledevice and ChromaDB. It supports semantic search, metadata filtering, and automated file organization for efficient media management.

Enables semantic search and question-answering over uploaded documents using vector embeddings and Google AI. Supports document organization with tags, section-aware queries, and hierarchical markdown structure preservation.

Provides AI agents with persistent, searchable memory that survives across conversations using semantic search, temporal versioning, and smart organization. Enables long-term context retention and cross-session continuity for AI assistants.

Provides a unified MCP interface that proxies requests to multiple backend servers including memory/knowledge graph and vector database services. Enables seamless access to distributed MCP tools through a single endpoint with automatic routing, health monitoring, and retry logic.

Enables vector-based document retrieval, extraction and chunking of arbitrary files into Markdown, and generation of private deep research reports from a configured pipeline. Connections are authenticated with an organization ID, token, and pipeline ID.