"World Health Organization" matching MCP servers:

Enables AI assistants to search, browse, and save to a semantic knowledge graph with vector search and hierarchical organization.

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.

Enables storing, searching, and discovering knowledge connections using associative memory, with semantic search, hierarchical organization, and cross-environment sync.

AI building world

A narrative graph engine that enables LLMs to generate, track, and mutate complex fictional worlds while maintaining consistency between factions, characters, and locations. It acts as a specialized RAG framework for storytelling, allowing models to manage thousands of entities without exceeding context limits.

Enables storing, searching, and managing code snippets using hybrid semantic search and keyword matching with automatic language detection and tag-based organization.

Enables agents to persistently record experiences, recall related episodes, run VRS convergence, and manage bounded world-state through MCP tools.

Freshness gate for AI agents: verify a belief is still true against the live world before you act.

Enables AI agents to create, search, and manage subject matter expert profiles, including private workspace organization and moderated sharing to a common library.

Enables intelligent RSS feed management with AI-powered semantic search, advanced filtering, and a comprehensive reading workflow. Supports OPML parsing, article organization with status tracking, and token-efficient browsing of large feed collections.

Enables querying a local health knowledge base with hybrid RAG (vector + FTS5) and exploring backlinks between notes.

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.

An AI agent system that monitors manufacturing equipment health using the Model Context Protocol (MCP). Enables answering natural language questions about equipment status, maintenance, and anomalies through MCP tools.

Transforms a Medium article archive into an intelligent writing assistant for book writing, enabling content analysis, theme extraction, and chapter organization.

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.

MCP server for a World Cup 2026 prediction game assistant, providing tools for live game data via PostgreSQL and semantic search over football articles via RAG.

Enables Figma-to-Code workflows by retrieving and calibrating real-world components from Figma designs, screenshots, and a component knowledge base, outputting component contracts and preview implementations.

A lightweight, AI-native server that enables GPT-based AI agents to fetch real-time customer and organization context from Zendesk APIs dynamically.

Hardened MCP bridge to the full Ollama ecosystem — local and cloud models. 6 tools covering health, model management, chat, and generation with SSRF mitigation, singleton HTTP client, and structured error handling. Two deps, one file.

Low-token MCP server for HPE Networking automation, enabling search of Aruba/HPE docs, OpenAPI details, Central health checks, troubleshooting workflows, configuration management, and guarded ArubaOS 8 migrations and GreenLake Platform operations across platforms like Aruba Central, ClearPass, Mist, and Apstra.