"Agentic RAG: Understanding or Exploring Its Meaning and Applications" matching MCP servers:

Enables persistent, searchable memory for Claude Code by storing session knowledge in a local PostgreSQL + pgvector database, with hybrid vector and full-text search, automatic session mining, and knowledge graph curation.

Enables cited, revision-aware retrieval over real IETF RFC chunks with zero LLM in the answer path, including graph-based supersession resolution and relationship queries via MCP tools.

An MCP-based multi-agent retrieval-augmented generation system that enables question answering over academic papers with hybrid search, knowledge graph multi-hop reasoning, and source-cited answers.

Provides RAG tools with local vector retrieval and web fallback using Firecrawl, enabling document ingestion and querying through MCP stdio transport.

MCP server providing tools for entity extraction, query refinement, and relevance checking to build Agentic RAG applications.

Enables querying internal documents via a FastAPI REST API and MCP server, using retrieval-augmented generation and an agentic loop that can invoke tools like document search and calculations.

Enables AI assistants to retrieve grounded, cited answers by first searching a local knowledge base and falling back to live web search when needed.

A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.

Agentic RAG Knowledge Assistant is a secure, tenant-isolated MCP server built with FastAPI, PostgreSQL, and pgvector that enables document ingestion, semantic retrieval, and vector search over PDF, DOCX, and text files through authenticated MCP tools.

An agentic Retrieval-Augmented Generation (RAG) system that combines a small curated machine learning knowledge base with real-time web search capabilities, powered by the Model Context Protocol (MCP).

A local Retrieval-Augmented Generation system that enables AI agents to query and retrieve information from document collections using ChromaDB vector search and Ollama LLMs through a FastAPI interface.

Enables agents to retrieve relevant context from local documents via MCP tools, supporting ingestion, semantic search, metadata filtering, and evidence inspection entirely on-device.

An intelligent Retrieval-Augmented Generation (RAG) application that uses the Model Context Protocol (MCP) to automatically decide between searching a private knowledge base or the web.

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

Enables hybrid search and agentic retrieval over Confluence pages via MCP tools, allowing Claude Desktop, Cursor, or Claude Code to search and retrieve Confluence content.

Enables querying Confluence documents through agentic reasoning over hierarchical document trees, replacing vector similarity with structural navigation.

Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.

Minimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.

RAG Vault lets your AI coding assistant search your private documents locally. Everything runs on your machine and your data stays private.

Enables coding agents to retrieve project context from an AnythingLLM RAG at session start and write back human-approved durable facts via five workflow-shaped tools.