"Developing a FHIR-Based Digital Health Application with EHR, PHR, and Phenotyping Frameworks" matching MCP servers:

Exposes a FHIR endpoint as deterministic read-only tools, providing a context layer for retrieving clinical data from FHIR servers.

Enables LLM-based agents to interact with FHIR healthcare data through natural language prompts, providing full CRUD operations on FHIR resources, document processing, and semantic search capabilities.

Enables AI assistants to query your local browsing history using keyword search, RAG Q&A, and activity stats.

MCP server for FHIR interoperability, enabling natural language querying and manipulation of clinical data with full CRUD operations, semantic search, and RAG capabilities.

Provides FHIR Implementation Guide facts via MCP tools, enabling querying of must-support, bindings, constraints, and value-set usage from StructureDefinition artifacts for conformance and testing support.

A persistent long-term memory system that enables AI clients to store and recall notes, code, and research via semantic search. It utilizes Google Gemini embeddings and Supabase pgvector to provide a secure, searchable 'Second Brain' for MCP-compatible applications.

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

Enables enterprise knowledge search through a chat interface, comparing traditional RAG with MCP-driven retrieval using hybrid BM25 and dense vector search, Ollama-powered answer generation, and question routing to domain-specific retrieval tools.

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

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.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Enables AI coding assistants to query private academic paper collections via standard MCP tools, with hybrid retrieval, reranking, and inline citations.

A simple application demonstrating Model Context Protocol (MCP) integration with FastAPI and Streamlit, allowing users to interact with LLMs through a clean interface.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

A Model Context Protocol server that exposes multiple AI tools over SSE transport with JWT-based secure authentication, allowing for dynamic tool registration and session management.

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

A multi-tool AI assistant system that uses Model Context Protocol to connect language models with various tools, including math calculations and weather information.

Enables integration with financial transaction data through REST APIs, PostgreSQL databases, and document storage systems. Demonstrates agentic AI capabilities by connecting to Alpha Vantage API and managing financial data through natural language interactions.

The MCP server provides tools for entity extraction, query refinement, and relevance checking to enhance RAG applications by integrating with OpenAI and Gemini.